Performance and UI improvements

We’ve shipped a round of performance and optimization improvements to Second Brain, along with a refreshed Learn feature dashboard and UI. Queries run faster, data loads more reliably, the overall experience feels tighter, and the Learn section is now easier to navigate and read at a glance.

Unified commerce order history

If you sell across multiple platforms, your customer data has probably always been scattered. Someone buys from you on Amazon, again on Shopify, and they exist as three separate records across three separate dashboards. Second Brain now solves that.

You can import full order history from any sales channel, including Amazon, Shopify, Faire, Walmart, eBay, and more, with support for multiple stores on the same platform. Orders and individual line items land in one place, tied to your contacts. Buyers are automatically matched across platforms by email, phone, or customer ID, so a customer who purchased from you on three different channels shows up as one person with their complete history.

Every platform exports data differently, so we built a universal database structure that can receive any of them. You bring your export file and describe it to Claude or Codex, and it maps your data into the right structure automatically. Once it’s in, you can ask for any report in plain language: which products sold most last quarter, which customers haven’t reordered in 90 days, lifetime revenue by channel. No dashboards to configure, no exports to run.

Contact details in the desktop app now show commerce orders alongside Stripe and PayPal activity, with products, quantities, and prices in one view, and commerce spend included in each contact’s lifetime revenue totals. Stripe and PayPal records now also capture gross, fees, net, refunds, and currency-converted amounts. Multi-currency is supported throughout.

If you’re not in ecommerce, none of this appears in your Second Brain. No commerce tables, no order fields. This is a core principle we’re building around: you only see what’s relevant to how you actually use the product. The Database tab works the same way. It shows only tables that contain your data, and the moment you import something new, that table becomes available to navigate, manage, and search. There’s more to do here, but the direction is set.

Your Second Brain, accessible from anywhere

Second Brain stores your data locally. Your SQLite database lives on your computer, not on a cloud server somewhere, which means you stay in control of it. That’s intentional. But it also raised an obvious question: what if I’m not at my desk?

We’ve added instructions for connecting Second Brain to Codex on the desktop. Once you set that up, you can access your knowledge base from two places: Claude Desktop on your computer, and the ChatGPT app’s Codex tab on your phone. As long as your computer is on, Codex on mobile can reach your local database through that desktop connection.

What that means in practice: you’re in a meeting, you remember you wanted to add a contact or update a note. You pull out your phone, open the Codex tab, and do it. Your computer stays home. Your data stays local. You just gained a remote interface into it.

What you can do from mobile

Anything you can do in Second Brain from the desktop, you can now do from your phone. Query your knowledge base in plain language, add new records, edit existing entries, look something up mid-conversation. The database never leaves your machine. What travels between your phone and your computer is only the query and the response. The data stays where you put it.

How to set it up

The connection instructions are inside your dashboard. Follow the Codex setup guide once, and mobile access works automatically from that point on.

Already using Second Brain? Download the latest version to get all of the above, including the unified commerce history, the refreshed Learn dashboard, and the Codex setup instructions.

New to Second Brain? Start a 7-day free trial and see how it fits into your workflow before committing to anything.

Want a walkthrough first? Book a free demo call and we’ll show you how the whole thing works, including the mobile setup.

I’m learning Spanish. It will be my fifth language. Every time I’ve learned a new one, I’ve started the same way: words. Not grammar rules, not conversation practice, not apps that gamify everything into streaks and badges. Just words, a few minutes a day, as consistently as possible.

Vocabulary is the foundation, and it’s the only part of language learning that’s genuinely measurable. You either know a word or you don’t. You can set a goal like “500 words this year” and actually track it, which is more than you can say for “get better at speaking.”

The problem is I’ve been counting manually. A spreadsheet here, a notebook there, trying different apps, none of them doing exactly what I needed. Most tools are built around flashcard decks or spaced repetition algorithms. Useful, but not the point. I just wanted a simple, honest count: words I know, words I’m working on, progress over time.

Then I started hearing the same thing from Second Brain customers. They were already using AI to learn languages, having lessons with Claude, building vocabulary through conversation. But the tracking was still a mess, manual counts, scattered notes, no real picture of progress. Same problem, same frustration. So we decided to just build it properly.

Introducing Learn

Second Brain now has a Learn section, built specifically for language study. It’s on by default when you open the app.

Here’s what it does:

Language profiles. Add one or more languages you’re studying. Each gets its own word list, phrase list, and progress tracking. You can archive a language you’re not actively working on and bring it back anytime.

Word and phrase tracking. Every word or phrase you add gets a status: new, learning, known, or ignored. When you mark something known, the date is recorded. That’s how your progress becomes measurable over time, not just a feeling.

A real dashboard. You get a progress summary showing total tracked items, known count and percentage, and weekly charts of words marked known over the last 8 weeks. You can filter by the last 7 days, last 30 days, or all time. It’s a simple, honest picture of whether you’re actually making progress.

Claude does the logging. This is the part that changes the workflow entirely. During a lesson with Claude, it can add words you miss, mark ones you already know, log phrases, and update statuses, all through your existing MCP connection. Everything stays on your machine. No cloud sync, same local-first model as the rest of Second Brain.

What This Looks Like in Practice

You open a lesson with Claude and tell it you want to practice Spanish vocabulary. During the conversation, Claude logs new words automatically. After the session you open Learn, check your dashboard, and see exactly where you stand.

You can also ask Claude directly: “How many Spanish words have I marked known this month?” and get a real answer pulled from your data.

A few starter prompts worth trying:

• “Add the top 100 most common Spanish words to my Learn list.”

• “Populate about 50 useful Spanish phrases, greetings, travel, and small talk.”

• “During our lesson, log new words I miss and mark ones I already know.”

• Already have a word list somewhere? Upload your spreadsheet or CSV and ask Claude to insert it into your Learn list. No manual re-entry.

Getting Started

Open Learn from the main menu (it sits after Settings). Go to Profiles and add your first language. Then update your Claude skill from the in-app skill setup page, that’s what gives Claude access to read and write your Learn data. After that, study with Claude or browse and edit your lists directly in the app.

If you don’t need Learn, you can hide it entirely from Settings without losing any data.

Is This for Everyone?

Not everyone is learning a language, and not everyone who does learns through vocabulary lists. That’s fine. If Learn isn’t relevant to you, you can hide it from Settings in one click and nothing changes about how you use Second Brain.

But if you are learning a language and you’ve been tracking progress manually or not tracking it at all, this is worth trying. The AI side was already covered. What was missing was the structure around it.

If you try it and have ideas for what else would be useful, I’d genuinely like to hear them. There’s more to build here.

Download the new version at brain.hexact.io. A 7-day free trial is included, no credit card required.

Email archive import was already in Second Brain. This update expands it significantly. We added support for more file types and removed the size limits that were blocking larger archives. If you tried importing before and hit a wall, try again.

Supported formats now include .eml, .mbox, .zip containing .eml files, and .pst for Outlook. The dry run option lets you preview the import before it runs, which is useful when you are dealing with a large archive and want to know what you are working with first.

Custom datasets

You can now create named datasets and import any CSV file into Second Brain. Price lists, product catalogs, inventory, whatever structured data you work with regularly. You can have multiple datasets and manage them independently.

Deduplication is handled by ID. Re-import an updated file and existing records get updated, not duplicated. Update your catalog, re-import, done.

Claude can query any dataset directly in conversation. Ask about your prices, look up a product, cross-reference a price list. No manual copying, no context window gymnastics.

Second Brain as a source of truth

The core idea behind Second Brain is that Claude should always have accurate, current context about your business without you having to paste it in every time. You build the knowledge base once, keep it current, and Claude works from it.

It works both ways. Claude reads from your knowledge base to answer questions and help you make decisions. You can also ask Claude to update records, retag entries, reorganize datasets. The app and Claude are two ways to manage the same data. As your business evolves, your knowledge base evolves with it.

Bug fixes and performance improvements

This release also includes:

• Bug fixes across import pipelines and dashboard rendering

• Better handling of large file imports without timeouts

• Stability improvements for MCP connectivity

• General performance improvements across the app

If you are on an older version, the update is worth it.

Download the latest version — available on macOS and Windows.

7-day free trial, no credit card required.

Want a walkthrough? Book a free demo call.

I’ve written a lot about why we built Second Brain and what problems it solves. But I keep getting the same question: “What exactly does it do?” Not the philosophy. Not the vision. The actual features, right now, in the current version.

So here’s the full breakdown. Everything that’s in the app as of today.

Your local database

Second Brain runs on a local database on your machine. No cloud. No subscription server. Your data sits in a file on your computer that you fully own and control.

The dashboard shows you what’s in your database: how many records, how much space it takes, and shortcuts to the main sections. You can browse every table in the database directly from the app, search across all your data from one search bar, and edit or delete individual records without touching any code.

When you first open the app, a short onboarding walks you through the basics. The app checks for updates automatically and tells you when a new version is available.

Contacts and CRM

This is where most of your data comes together. You get a full contact management system with search and filtering across names, emails, phone numbers, and organizations.

Each contact has a detail page with everything linked to them: identity info, email addresses, phone numbers, social profiles, physical addresses, company affiliations, newsletter subscriptions, billing records, calendar events, notes, and a timeline of all activity. You can see lifetime revenue per contact when payment data is imported (stripe, paypal).

Need a list? The CSV export lets you pick which columns to include, applies whatever filters you’ve set. That’s your email campaign list, or your outreach segment, built from real data in seconds.

21 import sources

We built parsers for the tools people actually use, so you can bring existing data into the database without reformatting anything.

Payments and billing: Stripe subscriptions, Stripe transactions, PayPal subscriptions, PayPal transactions, and bank statements (CSV). Each one scoped per company. Bank statements use a column mapping step so you can import from any bank’s CSV export format.

Email: Email suppression lists (bounces, unsubscribes, spam reports, blocks) and full email archives.

Contacts: A universal CSV importer with column mapping that works with any CRM or spreadsheet export, plus dedicated parsers for Google Contacts and survey responses.

Calendar: Google Calendar event imports.

Publishing: Substack archive imports with author attribution for articles.

Notes and knowledge: Notion (Markdown and CSV), ChatGPT conversation exports, Evernote (.enex files), OneNote (multiple formats), Obsidian vaults, Logseq graphs, and general knowledge files (PDFs, Word docs, text files, ZIP archives with 200+ documents at once).

Project and task tools: Asana tasks, Jira issues (all fields), Trello boards, and Todoist backups.

Every import that supports it has a dry run mode. You can preview exactly what will be created or changed before anything gets written to your database. Background imports show progress in real time with status updates and logs, and you can cancel mid-import if something looks wrong.

Database management

You can browse every table in your database. Edit any row directly. Delete individual records or bulk delete. Deletion shows you exactly what will be affected before you confirm.

The maintenance tools handle the cleanup work that databases need over time. There’s a cleanup tool that removes junk contacts (disposable email domains, spam-pattern names), tags suppressed emails, and refreshes database indexes. You can customize the blocklist of disposable domains and define your own junk name detection rules.

Backup and restore is built in. Export your entire database as a backup file.

Live automations

Three integrations that keep your database current while the app is running.

Hexomatic. Connect your API key, pick a scraping workflow, and map it to a company and a target field in your database. Hexomatic runs on its schedule, and the results flow into your local database automatically. No manual downloads. You can pause and resume sync, view activity logs, and manage multiple workflow mappings.

Stripe. Enter your secret key per company, and Second Brain syncs your payment data automatically. Pause, resume, or trigger a manual sync anytime. When Stripe automation is active, the manual Stripe CSV import on the Import page is disabled to avoid conflicts.

PayPal. Same setup. Client ID and secret, live or sandbox mode, pause and resume, manual sync.

Claude Desktop connection

This is what turns a database into a working AI tool.

Second Brain generates the MCP configuration snippet that connects your local database to Claude Desktop (watch this video to learn how to connect to Claude Desktop). One setup step, and Claude can read from and write to your local knowledge base directly inside a conversation.

The app also generates a starter Skill file, a markdown document that teaches Claude how to work with your data safely. Permission-first rules: Claude can read anything, but writes only happen when you explicitly ask. The Skill explains how it works, and you can customize to your specific needs. See more skills here.

Claude Desktop’s free plan works with this connection. You don’t need a paid Claude subscription to start using your database as context.

Settings and profile

Your owner profile holds your email addresses, phone numbers, companies and roles, social profiles, and other details. This is the context Claude uses when it needs to know who you are.

You can manage multiple owner companies, which is how imports, automations, and contact scoping all stay organized when you run more than one business.

CRM settings let you define your own relationship types for contact imports and customize field labels for column mapping, so your import workflow matches your business terminology.

What’s new in this version

We ship updates regularly. The latest version includes stability improvements across import pipelines, better error handling on large datasets, and refinements to the database management tools. If you’re on an older version, download the latest build.

Try it

Download Second Brain at brain.hexact.io. 7-day free trial, no credit card required.

Start with one import. Whatever data you already paste into AI conversations manually. Get it into the database, connect Claude, and ask a question you couldn’t answer before.

That’s usually enough to see whether this changes how you work.

Want a walkthrough first? Book a free 15-minute demo.

It won’t.

AI is great at processing text and data. But every model has a context window, a limit on how much it can hold in memory at once. When your input exceeds that limit, the model either truncates it silently, loses track of earlier content, or gives you a partial result and stops. The capability is there. The capacity isn’t.

That’s the problem the Second Brain solves. It sits between your raw data and Claude, breaking large inputs into structured, queryable records in a database. Instead of one massive file that overwhelms the context window, Claude sees clean rows it can process, search, and cross-reference without hitting any limits.

Here are two real use cases from my own experience that show exactly how this works.

Use Case 1: Merging Contacts from Multiple Sources into One Clean Database

If you run more than one business, or even one business with more than one data source, you know this situation. Customers in one spreadsheet, leads in another, investors in a third, maybe a fourth export from some CRM or platform you used two years ago. Every file has different columns. Some split first name and last name. Some have a single “Name” field. One has phone numbers, another doesn’t. Formats are inconsistent. Duplicates are everywhere.

At some point you need a unified view. Who are these people? Where do they overlap? Who is a customer in one business and an investor in another?

This looks like a straightforward AI task. Upload the spreadsheets, ask Claude to merge them. Done.

Except it doesn’t work at scale. If your dataset is a few hundred rows, AI can handle the merge. But when you’re dealing with thousands of contacts across multiple exports, the model truncates data, hallucinates entries, or gives you a partial result and acts like it’s done. Large CSVs exceed what any chat-based AI can process in one shot.

How the Second Brain makes this work

The Second Brain treats contacts as a proper database, not a chat attachment. Each contact is a record in a SQLite table. Each field (name, email, phone, company, source, payment history) is a column. When you import from multiple spreadsheets, the data gets normalized into one unified schema.

Now Claude can do the job it couldn’t do before.

Query across sources: “Show me everyone who is both a paying customer and an investor.” That’s a SQL join. Claude runs it in seconds.

Deduplicate properly: Match on email, name + company, or phone number. You define the rules. The AI doesn’t guess.

Enrich on clean data: “Which contacts are missing an email address?” “Which customers from 2023 never logged in again?” These questions are easy when the data is structured. They’re impossible when you’re staring at five different Google Sheets.

Sometimes the source data is even messier than mismatched columns. I’ve dealt with CSV exports where everything was stuffed into raw JSON payloads. Name, phone, address, service history, review status, all buried inside a single field. The Second Brain extracts and normalizes this automatically. You go from unreadable blobs to clean, queryable records, and suddenly you can ask things like “show me all clients in a specific city who completed more than 3 jobs but never left a review” and get an answer in seconds.

The AI didn’t get smarter. The data got structured. That was the only difference.

Use Case 2: Proofreading a 200+ Page Book

I wrote a book called “Bones: 21 Clichés Running and Ruining Your Business.” After months of writing and rewriting, I needed a serious proofread. Not just grammar. English is not my native language, and when you produce 200+ pages, small issues compound. A slightly unnatural preposition, an odd word choice, a sentence structure that’s technically correct but sounds off to a native ear. Over that many pages, these pile up.

Proofreading a book. That’s exactly what AI should be able to do. Upload the document, ask Claude to review it. Simple.

If you haven’t tried this with a long document, let me save you the frustration. It does not work.

Why large documents break AI

Large language models have context windows. Even the biggest ones cannot hold a 200+ page document in working memory at once. Here’s what actually happens.

The model reads the first chunk and does solid work on it. By the page 20-30 of the book, it’s lost track of tone and style decisions from the first chapters. By the end, it’s proofreading in isolation, with no awareness of the whole.

The response hits its output limit long before the job is finished. You get corrections for the first 20 or 30 pages. Then it stops. You ask it to continue. Now it’s working from a fragmented context. Quality drops with each continuation.

Formatting gets in the way. Word documents carry metadata: styles, headers, footers, comments, tracked changes. The AI confuses structural elements with content. Edits break your formatting. Sections get skipped entirely.

The result: maybe 15% of your book gets reviewed, inconsistently, with no guarantee the same standards applied throughout.

How the Second Brain makes this work

Instead of feeding Claude one massive .docx file, I just imported the book into the knowledge base. Each page became one row in a database table.

Now Claude wasn’t processing a 200-page Word document. It was looking at a table with 200+ rows of clean text. No formatting noise. No embedded images. No metadata. Just the words.

The real power showed up in cross-document queries. “Find every instance where I used ‘which’ instead of ‘that’ in a restrictive clause.” “Flag pages where the tone shifts noticeably from the pages around them.” “List every sentence longer than 35 words.” These consistency checks only work when the full text is structured and queryable. They’re impossible with a monolithic file.

The proofreading quality stayed high from page 1 to page 211. The AI never had to manage a large document. It just did what it’s good at: analyze text, one piece at a time, with the ability to reference everything else when needed.

Why This Keeps Happening

These two use cases look completely different. One is data merging. The other is prose editing. But the failure mode was the same: the AI had the skills for the job, but the input was in a shape it could not work with.

AI models are excellent at analyzing, comparing, and transforming organized data. They struggle when the first task is organizational.

The Second Brain fills that gap with two layers: structure and Skills.

Structure is the road. Your raw data gets normalized into a queryable database so AI has something to work with.

Skills are the rules of that road. A Skill is a set of instructions attached to your Second Brain that tells Claude how to behave with your specific data. Which tables exist. How sources relate to each other. What to never guess about. What to ask before doing. It’s the rulebook for your knowledge base.

This is why “upload your file and ask AI” keeps disappointing people. No structure, no rules. You’re asking a powerful engine to drive without a road or a map. The Second Brain provides both.

Where This Is Going

We started building the Second Brain to solve our own problems at Hexact. Since then, we’ve been using it to store and query across all of our published articles, track customer and lead data in one place, and connect ideas across different knowledge domains.

It works with the tools we already have. Hexomatic collects and enriches the data. The Second Brain stores and structures it. Claude analyzes it on demand. The combination is more useful than any of those pieces alone.

We’re still early in development, but if you’re dealing with scattered data across multiple sources, documents too large for AI to handle, or knowledge that needs structure before it becomes useful, this is what we’re building toward.

Want to see how it works? Book a call with us and we’ll walk you through the current setup, from data import to your first cross-source query.

Already using Hexomatic for data collection? The Second Brain adds the storage and analysis layer that turns scraped data into a working knowledge base.

Reality was the opposite. Every tool I use keeps its own version of reality. Stripe knows who paid. Substack knows who reads my newsletter. Google Calendar knows who I met with last Tuesday. My CRM knows... whatever someone remembered to type into it six months ago.

Now multiply that by several businesses, each with its own Stripe account, its own calendar, its own contact list, its own CRM. The data doesn’t just live in different tools. It lives in different worlds. A customer in one business might also be a client in another, and I’d have no way of knowing unless I happened to remember their name.

None of these tools talk to each other. And I got tired of it.

So when we built Second Brain, the first thing I did was dump everything into it. Every contact, every transaction, every subscriber, every meeting. All into one local database on my laptop. Then I connected it to Claude and started asking questions I’d never been able to ask before.

I finally know the relationship between my newsletter and customers

Here’s a question I couldn’t answer until I put both datasets in the same database: what’s the relationship between reading the newsletter and becoming a customer?

Not just the overlap. The sequence.

Did people subscribe to the newsletter first and then become customers? Or did they buy first and subscribe later? How long between subscribing and paying? Are long-time readers more likely to stay on a paid plan?

No single tool gives you this. Your newsletter platform tracks opens and clicks. Your payment processor tracks charges. But the timeline across both? That only exists when the data is in one place and you can actually ask questions across it.

In a traditional CRM, you’d need to connect your payment system to your analytics platform to your email tool, set up automations, build reports. In Second Brain, I asked the question and got the answer in about five seconds.

I actually know who I’m talking to

You’re about to get on a call. You check the CRM. You see a name, a company, maybe a deal stage. That’s it.

What you don’t see: this person’s payment failed twice last month. They filed a support ticket that’s still open. They unsubscribed from your newsletter.

You walk into the call thinking everything is fine. It’s not.

I used to do this all the time. Now before any important call, I ask Claude: “What do I know about this person?” It pulls their payment history, subscription status, newsletter engagement, calendar meetings, any notes I’ve saved. All in one answer.

It takes ten seconds and it completely changes how I approach the conversation. I’m not guessing anymore. I’m prepared.

You can ask questions nobody built a dashboard for

This is the thing that changed my thinking the most.

Dashboards are great, but they only answer questions someone already thought to ask. They’re static. Someone designed them last quarter based on what seemed important then.

That shift matters more than any feature. You go from consuming pre-made reports to interrogating your own business in real time. You become curious about your data because the cost of asking a question (before it was a new custom report) dropped to zero.

It works both ways

Everything I’ve described so far is about asking your database questions. But you can also tell it things.

After a call, I say “save this ….. about…..” It goes straight into the database as a note, linked to the contact. I can paste a screenshot of a chat and ask Claude to create a records based on that. I can paste a meeting summary and say “add this to the customer’s record.” I can say “this person is now the CEO of Acme Corp” and it’s done.

This is the part that makes it feel like that movie scene I mentioned at the top. You’re not filling out forms. You’re not clicking through dropdown menus. You’re having a conversation with your database, and it works in both directions. Ask and get answers. Tell and it remembers. Meanwhile still having the direct access to your data using Second Brain interface.

It keeps getting better

We ship updates constantly. Right now Second Brain has 21 import sources: Stripe, PayPal, Notion, ChatGPT conversations, Evernote, Asana, Jira, Trello, Obsidian, Google Calendar, Google Contacts, Substack, email archives, CSV uploads, and more.

We just added zip file imports (up to 200+ doc files at once), and improved database cleanup tool. More import sources are coming.

Here’s what I’ve learned building this: each new import source makes everything else more valuable. Connecting your Stripe data is useful. Importing your Stripe data, CRM and subscribers is powerful. Import your Google Calendar data on top of that and now you can see the correlations.

It compounds. The more data you bring in, the more questions you can ask, and the more patterns you find that were always there but invisible.

Try it

Download the latest version at brain.hexact.io. Free trial, no credit card.

Start with one import. Whatever you have. A contacts CSV, your transactions, your newsletter subscriber list, multiple spreadsheets. Get it in, connect Claude, and ask your database something you’ve always wanted to know but never could.

You’ll be surprised what’s been hiding in your own data.

Database management tools

You can now edit and delete data directly from the app. Browse your entries, fix a typo, remove something outdated, all from within Second Brain. This was one of the most requested features since launch, and it’s live now.

OneNote import

If you’ve been sitting on years of OneNote notebooks, you can now bring them into Second Brain. Export from OneNote, import into the app, done. Your notes land as structured entries in your local database, searchable and ready to connect with Claude.

Bug fixes

Several stability fixes across the board. Smoother imports, fewer edge cases on large datasets, better error handling. Nothing dramatic, but the kind of work that makes the app feel more solid every week.

Updated: How to connect with Claude

The setup instructions for connecting Second Brain with Claude Desktop have been updated. If you tried connecting before and hit friction, it’s worth revisiting.

Here’s the thing most people don’t realize: Claude Desktop’s free plan works with Second Brain. You get a limited number of prompts per day, but the MCP connection works the same way. You don’t need a paid Claude plan to start using your personal database as context. Install Second Brain, connect it to Claude Desktop, and you’re running.

The paid plan obviously gives you more prompts and longer conversations. But if you want to test the workflow before committing, the free option still works.

We also published a skills archive on GitHub with ready-to-use skills like weekly review, meeting prep, and quick lookup.

How to actually start

I keep hearing the same question: “What should I import first?”

Stop thinking about it as building an archive. Think about it as eliminating repetition.

What files do you upload to ChatGPT or Claude every week? What context do you re-explain every time you start a new conversation? Product specs, client data, meeting notes, project documentation, your own writing. That’s your starting point.

Import the stuff you’re already feeding to AI manually. Once it’s in your Second Brain database and connected via MCP, Claude has that context automatically. No more copy-pasting the same brief into every conversation.

You don’t need to import everything on day one. Start with what you use today. Add more as you go.

What’s coming next

We’ve gotten a lot of requests and ideas from early users (keep them coming). Two things we’re thinking about first:

Custom database location. Right now the SQLite database lives in a default directory. We want to give you the option to choose where it sits. Your NAS, a synced folder, an external drive. Your data, your choice of where it lives.

More tools to keep your Second Brain current. The initial focus was on importing historical data. The next step is making it easier to keep things up to date. Live syncs, scheduled refreshes, and smarter ways to capture new information as it happens, not after the fact.

Both are in active development. No dates yet, but these are top priority.

Try Second Brain: brain.hexact.io. 7-day free trial, no credit card required.

Want a quick walkthrough? Book a 15-min demo.

github.com/HEXACT-INC/second-brain-claude-skills

A skill is a markdown file that teaches Claude how to handle a specific task with your data. Drop it into your skills folder, restart Claude, and it works.

The repo currently have five skills:

second-brain is the core skill. It gives Claude safe, permission-first access to your local database. Read anything, write only when you say so. This one should be installed first. Everything else builds on top of it.

quick-lookup answers “what do I know about X?” questions. Ask about a person, a company, a deal, and it pulls everything from your KB, synthesizes it into a short answer. Useful before calls, before signing contracts, before replying to an email you’re not sure about.

weekly-review generates a summary of everything you captured in the last 7 days. Notes, meetings, saved ideas, new contacts. Instead of scrolling through your database wondering what happened last week, you get a concise brief.

meeting-prep builds a pre-call brief for a specific person or company. It pulls contact history, past meetings, relevant notes, payment data if it exists. You walk into the call knowing what matters.

save-and-categorize handles “save this” requests. Share a thought, a link, a meeting takeaway, and the skill figures out the right entry type, writes a clean title, and stores it properly. No manual tagging.

Tell Us What’s Missing, What’s Blocking You, or What You’d Change

We shipped the app and the skills based on what we already know. But you’re using it differently. You loaded different data, you run a different business, you ask different questions. That means you’re hitting walls we haven’t hit yet.

What feature would make the biggest difference for you right now? What’s confusing? What workflow are you trying to build but can’t? What skill would save you time if it existed? A daily journal, a deal tracker, an invoice reviewer, a hiring pipeline brief, a content calendar. If it involves structured data and a repeatable task, it’s probably a good skill. Reply to this email or leave a comment.

If you've built a skill that works for you and you're willing to share it, the repo is open. Fork it, add your skill folder with a SKILL.md and a README.md, and open a pull request. Same goes for improving the existing ones. One rule: keep each skill focused on one job.

Any feedback that points to a product gap - we’ll build it. That’s how the new import formats, database management tool and global search happened. Users asked, we shipped.

New Release: Download the Latest Version

We also shipped a new version of Second Brain today. If you already have the app installed, download the latest build from brain.hexact.io. Here’s what changed:

Database management tool. You can now edit any table in your database manually, right from the app. No SQL required. This was the most requested feature after launch.

Global search. Search across all your local records from one search bar. Contacts, notes, articles, transactions. Type a name or a keyword and see results from every table.

Bug fixes. Stability improvements across import pipelines, dashboard rendering, and MCP connectivity.

If you’re on an older version, the update is worth it. The database tool alone changes how you interact with your data day to day.

Use Cases That Make This Click

I’ve been using Second Brain daily since before the launch. Here are some real workflows that show what this setup can do once your data is loaded.

Pre-meeting intelligence. Before any call, I ask Claude: “What do I know about this person?” It pulls their contact record, any past emails, payment history if they’re a customer, notes from previous meetings. I walk in prepared without spending 15 minutes digging through apps.

Customer health check. “Show me all customers who paid more than $500 but haven’t logged in since 2024.” That query runs against my actual database. Real data, real answers. No dashboard needed.

Content research from your own writing. “What have I written about data ownership?” Claude searches my 168 published articles and pulls the relevant ones. I see my own arguments, positions, and examples. Useful to see how things change compared to what I said six months ago.

Supplier and vendor comparison. If you use Hexomatic to scrape pricing pages or product catalogs, that data flows into Second Brain automatically. Ask Claude to compare suppliers by price, minimum order, or any other detail. The comparison runs on your scraped data, not on Claude’s general knowledge.

Trend spotting across scraped data. Scrape job postings in your industry weekly. After a few months, ask Claude: “Are companies hiring more for AI roles compared to three months ago?” You get trend analysis from your own data, not a generic report.

The pattern is the same in every case. Your data is already in the database. You ask a question in plain language. Claude reads your actual records and answers. No exports, no spreadsheets, no switching between six apps.

Get Started

1. Download the latest version at brain.hexact.io

2. Install the second-brain skill, then add whichever specialized skills you want

3. Browse the full repo at github.com/HEXACT-INC/second-brain-claude-skills

Three things you can do right now: update the app, try a new skill, and tell us what you’re building with it. Reply to this email with your use case, open a pull request with your skill, or just star the repo so we know you’re watching. We’re building this in the open and your input shapes what comes next.

When you link Hexomatic to Second Brain, scraped data flows directly into your local database while the app is running. No exports. No file management. No copy-pasting into Claude. The data arrives, gets indexed, and becomes queryable immediately.

That changes what scraping is useful for.

Why This Connection Matters

Connect them and you get a knowledge base that updates itself. Hexomatic collects, Second Brain stores, Claude reads. You ask questions and get answers based on data that was scraped hours ago, not months ago.

The key difference from the workflow I described in the previous article (scrape with Hexomatic, then pass CSVs to ChatGPT or Claude manually) is that this is continuous. You set it up once. The data keeps flowing in. Your AI gets smarter about your market without you touching anything.

Use Cases

Competitor price monitoring

Set up a Hexomatic workflow that scrapes competitor pricing pages on a schedule. Every run pushes the latest prices into Second Brain.

Then ask Claude: “How [product name] prices changed in the last 30 days?” or “Which competitors or products or items raised prices this quarter?” Claude answers from real data, not from its training set. No hallucination. No guessing. Actual numbers from actual pages, timestamped and stored locally.

Lead research and qualification

Use Hexomatic’s Google Search Scraper to find companies in a specific niche. Extract team pages, contact information, company descriptions. All of it flows into Second Brain.

Now you can ask Claude: “Show me all companies that have [specify question] and mention Series A funding” or “Which leads from the Miami construction scrape have email addresses on their contact pages?” Claude queries your local database and gives you a filtered list. No spreadsheet filtering. No VLOOKUP.

Market research across job boards

Scrape job postings in your industry. Track what roles companies are hiring for, what tools they mention, what salary ranges they post. This data accumulates in Second Brain over weeks and months.

Ask Claude: “What programming languages are most demanded in the fintech job postings I scraped?” or “Are companies in my scrape hiring more for AI roles compared to three months ago?” You get trend analysis based on your own collected data, not a generic industry report.

Supplier and vendor intelligence

Scrape supplier directories, vendor lists, product catalogs. Hexomatic handles the extraction, Second Brain stores it.

Ask Claude: “Which suppliers from the last scrape offer bulk pricing under $5 per unit?” or “Compare the three packaging suppliers I scraped last week by product range and minimum order.” The comparison happens across your actual scraped data, not from Claude’s general knowledge about those companies.

Real estate and property data

Scrape property listings, Zillow pages, commercial real estate directories. Price, location, square footage, listing date. All flows into Second Brain.

Ask Claude: “What’s the average price per square foot for commercial properties I scraped in Broward County?” or “Show me listings under $500K that appeared in the last two scrapes.” You’re doing market analysis on live data without building a dashboard or writing any code.

Review and reputation tracking

Scrape Google Maps listings, Yelp reviews, Trustpilot pages for your business or your competitors. The reviews accumulate in your knowledge base.

Ask Claude: “What are the most common complaints in our competitor’s reviews from the last month?” or “Has our average rating changed since the last scrape?” Sentiment analysis on real customer feedback, stored locally, queryable anytime.

Content and SEO monitoring

Scrape competitor blogs, industry publications, ranking pages for target keywords. Track what gets published, how often, and what topics they cover.

Ask Claude: “What topics did our top three competitors publish about this month?” or “Are there any keyword gaps where competitors are publishing but we’re not?” Content strategy informed by actual competitive data, not assumptions.

How It Works in Practice

The setup is simple:

1. Build your scraping workflow in Hexomatic. Define what to collect and how often.

2. Connect Hexomatic to Second Brain through the integration settings.

3. Leave Second Brain running. Data flows in automatically with each Hexomatic run.

4. Ask Claude anything about the data you’ve collected.

There’s no batch processing step. No file conversion. No manual import. The pipeline is: web page to Hexomatic to local database to Claude. All automated except the part where you ask questions.

The Compound Effect

The real value isn’t in any single scrape. It’s in accumulation. One scrape gives you a snapshot. Months of scheduled scrapes give you a dataset that shows trends, changes, and patterns that no single search could reveal.

Because Second Brain stores everything locally with timestamps, Claude can compare across time periods. “How has this changed since January?” becomes a real question with a real answer, not a prompt that returns a generic framework.

Every Hexomatic workflow you connect to Second Brain makes your AI more informed about your specific market, your specific competitors, your specific opportunities. That’s data no one else’s Claude has access to.

Many of these scraping tasks already have built-in automations in Hexomatic, including Google Maps, Google Search, Amazon product data, and readable page content extraction, emails scraper, etc, so you just select the automation and run it. For more specific cases where you need a custom scraping template, you can build it yourself or let our team handle it through the Concierge Service.

→ Get started with Hexomatic at hexomatic.com → Download Second Brain at brain.hexact.io

Two years ago, knowing how to write a financial model or draft a legal contract was a real skill. Now you type a sentence and get a usable first draft in seconds. The knowledge itself stopped being scarce. Anyone with a $20/month subscription has access to the same reasoning engine you do.

So what actually makes you different?

Your data. Your customer transactions, your payment history, your CRM contacts, your project tasks from Asana and Jira, your notes from Notion and Obsidian, your email archives, your calendar events, your ChatGPT conversation history, your internal documents. The specific, exact, up-to-date information that no public model was trained on. That’s the only thing the next person with the same AI subscription doesn’t have.

The problem with AI right now is not that it’s dumb. It’s that it guesses when it should know. Ask it how many customers you have, and it makes something up. Ask it what you decided about pricing last quarter, and it hallucinates a plausible answer. Ask it who your biggest client is, and it gives you a confident response based on nothing. AI doesn’t have your actual data. It has outdated patterns from the internet. So it fills the gaps with things that sound right but aren’t.

General context features like memory and custom instructions don’t fix this. They store surface-level preferences. They don’t give AI access to your exact records, your real numbers, your actual transaction history. The difference between “I remember you run a SaaS company” and “here are the 12 accounts that churned last quarter and why” is the difference between a chatbot and a tool you can make decisions with.

That’s what Second Brain is. A local database on your machine that gives AI persistent access to everything you’ve collected. All queryable through a conversation with Claude. Nothing leaves your machine. You own the data completely.

Instead of “what’s a good pricing strategy for SaaS?” you get “based on your last three pricing changes and how churn responded each time, here’s what the data suggests.” One is a blog post anyone could read. The other is business intelligence built from your actual records.

Getting Your Data In

The biggest barrier to any system like this is the import step. If it’s painful, people don’t do it. So we built parsers for the tools people already use.

Second Brain currently imports from: CRM exports, Stripe, PayPal, Notion, ChatGPT conversation exports, Evernote, Obsidian vaults, Logseq graphs, Asana tasks, Jira, Trello boards, Todoist, Google Calendar, Google Contacts, Emails and general knowledge files (PDFs, Word docs, text files, ZIP archives).

Each parser takes the export format the platform already provides and converts it into structured, searchable entries in your local database.

Beyond static imports, Second Brain connects directly to Hexomatic. You set up scraping workflows in Hexomatic, define what to collect and how often, and the results get inserted into your local database automatically while the app is running. Competitor pricing data, Google Maps listings, Google search results, job postings, product catalogs, articles, etc. The data flows in without manual downloads or copy-pasting. Your knowledge base stays current without you doing the research yourself.

Where We’re Headed

Better tools to manage your knowledge base

Importing data is step one. Organizing it so AI can reason over it properly is step two.

Right now, imported data lives as entries. That works for search. But as your knowledge base grows, a flat collection of thousands of entries becomes hard for AI to navigate efficiently. Structure matters. A database where contacts link to meetings, meetings link to decisions, decisions link to tasks gives AI something it can actually reason across. A pile of disconnected files doesn’t.

We’re building tools to shape that structure. Tag systems, category management, duplicate detection across sources, merging entries that came from different platforms but represent the same thing. The goal is to give you control over how your knowledge base is organized without requiring you to write SQL or think about database design.

We’re also improving how AI reads the correlations in your data. When you ask about a customer, Claude should be able to connect their payment history, the meeting where you discussed their account, the task you created afterward, and the email you sent. That requires the database to understand relationships between entries, not just store them side by side.

More import sources

We're adding connectors for more categories: calendar and scheduling tools, accounting and bookkeeping platforms, email marketing services, e-commerce systems, team messaging, and contact management. Every new category feeds the same local database and gives AI a fuller picture of your business.

Optional cloud sync

Second Brain is local-only by design. That's the right default and it's not changing. But we're already working on the architecture for an optional cloud layer. It's a harder problem than it sounds, because keeping the same privacy model in a cloud environment requires deeper engineering than just syncing files. We're building it properly, not rushing it. Given how fast things are moving, this is not far out.

This is going to be opt-in. Local-only remains the default.

The Point

The advantage is no longer which AI you use. It’s what you feed it. That compounds over time, and it only works when AI reads from your real data, not a pasted paragraph that disappears when you close the tab.

→ Get Second Brain at brain.hexact.io

Download it at brain.hexact.io and subscribe for updates.

Why we built this

Every business runs on scattered data. Contacts in one place, billing in another, notes in a third, emails buried somewhere else. We built Second Brain because we needed it ourselves at Hexact. The idea is simple: one local app that pulls everything together and lets you query it, connect it, and actually use it.

It started as an internal tool. Now it’s available to everyone.

What you get right now

Second Brain ships with a working set of features across six areas. Here’s what’s already in the app.

Your local knowledge base

Everything runs on a local SQLite database on your machine. The dashboard gives you a quick snapshot of what’s stored and where your database lives. You can browse your data table by table with the built-in database explorer. Light and dark modes are included.

Data imports

You can bring data in from multiple sources:

• Stripe and PayPal billing data from CSV exports (subscriptions and payments)

• Email archives from common mailbox formats (individual messages, mailbox bundles, zipped bundles)

• Google Contacts and Google Calendar exports

• Survey responses from CSV

• Substack content exports

• Knowledge files (documents you want searchable in the system)

• A universal CSV import for anything else you want to map in

Every import supports dry run mode, so you can preview parsing results before anything gets written.

Contacts hub

A dedicated contacts area with multiple ready-made views: recent, newsletters, missing email, by source, businesses, and more. Search works across large datasets. Each person gets a profile with a timeline view of related activity, including calendar context where available. You can edit records, export a person as a portable summary, delete, or suppress contacts you don’t want surfaced.

Writing and notes

An entries system for notes, records, and long-form writing. Articles get their own filtered view for longer content. Everything lives in the same underlying system, just different lenses on the same data.

Live automations

While the app is open, background sync runs periodically. You can connect:

• Hexomatic (workflow mapping) With Hexomatic, you can set up web scraping workflows that run on a schedule. The scraped data gets inserted into your local database automatically, no manual exports or copy-pasting. You define what to scrape, map it to a company, and the app handles the rest in the background while it's running.

• Stripe and PayPal (API key per company, billing sync)

Claude Desktop integration

This is the part that makes Second Brain more than a database. You can connect Claude Desktop directly to your local knowledge base. The app walks you through the setup right on the main page. It also generates a starter skill document that teaches Claude how to work with your data safely, with permission-first rules baked in.

Once connected, you can talk to Claude about your contacts, customers, articles, notes, and billing data as if it already knows your business. It reads directly from your local database. It can also write to it. Ask Claude to add a contact, update a record, save a note, or log a meeting, and it does it right in your knowledge base. The skill document enforces permission-first rules, so nothing gets written without your explicit say-so.

Settings and housekeeping

Owner profile with multiple emails, phones, companies, roles, and domains. Timezone selection so dates read correctly. Database cleanup tools to remove junk and duplicates, with tuning for blocked email domains and junk name patterns.

This is an early release

Some things still need polishing. We’re shipping updates regularly. If something feels off or breaks, please contact support@hexact.io. Feedback shapes what gets built next.

Download Second Brain at brain.hexact.io.

But first I want to talk about something that influenced how we designed this.

Why not just use OpenClaw

If you follow the AI space, you’ve probably heard about OpenClaw. It can send emails, browse the web, negotiate deals, create social media profiles, all while you sleep. People are building teams of 9 agents that run their work and personal life simultaneously.

I genuinely liked the idea. Local-first, open-source, persistent memory. The philosophy is right.

But I wasn’t ready to hand over my business data to an autonomous agent that runs 24/7 and acts on its own. Not because the technology is bad. Because the trust model isn’t there yet for me. My database has customer payment records, contact details, internal notes, financial data across three companies. The moment an agent starts acting autonomously on that data, sending emails I didn’t review, making decisions I didn’t approve, modifying records while I sleep, I lose control.

Many people are raising concerns about OpenClaw’s broad access to files and terminal. One user’s agent created a dating profile without his knowledge. These are early-stage problems that will get solved. But right now, for real business data, I wanted something different.

So we built Second Brain with a different philosophy: give AI full read and write access to your data, but only when you’re in the conversation. Nothing happens without you asking for it. Nothing runs in the background by AI, but you still have workflows running in the background. You stay in control of every query and every change, and you keep the track.

Same local-first approach. Same privacy model. But you’re the operator, not the passenger.

The database layer

Second Brain runs on a local database on your computer. No server, no cloud. You can open it in any database viewer, back it up by copying a file, or move it to another machine by dragging it over.

The schema is designed around how businesses actually organize information. Contacts, email addresses, phone numbers, organizations, relationships between people and companies, customer records with spend tracking, meeting notes, hiring and interview data, articles, documents, and structured notes. It’s not a generic “put anything in a table” setup. The schema has real structure so AI can reason over it properly. When Claude sees a contact with linked email addresses, payment history, organization affiliation, and meeting notes, it can answer questions that cross all of those dimensions.

The most important part is the application that runs locally. You can search, filter, browse, and manage your data without touching the command line.

Getting data in

This is the part that matters most, because a database is only useful if it’s easy to fill.

Second Brain has import parsers for multiple sources and formats. CSV files, ZIP archives, platform exports.

What you can import right now: contacts from any CRM or platform that exports CSV. Stripe transaction and subscription data. Customer records with spend data. Platform-specific exports like calendar events and more. Documents and files with text extraction.

The import pipeline doesn’t care where the data came from. If you can get it into a CSV or a supported export format, it goes into the database cleanly. We also implemented database optimization and cleanup functions.

Hexomatic integration

This is where Second Brain connects directly to what we’ve been building at Hexact for years.

Hexomatic was always a tool for collecting structured data from the web. Competitor pricing pages, Google Maps listings, job postings, product catalogs, lead information, news and articles.

Second Brain now connects directly to the Hexomatic API. You set up your scraping workflows in Hexomatic, define the schedule, and the results flow into your Second Brain automatically. No manual downloads. No copy-pasting between tools.

Your competitor changes their pricing? It’s in your database before your morning coffee. A new business opens in your market? Your local database gets updated. A job posting appears that signals a competitor is building a new team? Logged and queryable.

The combination of the two products creates something neither could do alone. Hexomatic collects. Second Brain stores, organizes, and makes it queryable through Claude. The scraping engine becomes the supply chain for your AI knowledge base.

The Claude connection

Second Brain connects to Claude Desktop through MCP (Model Context Protocol). This is the open standard that lets Claude interact with external data sources directly inside a conversation.

Once connected, Claude can read from your database and write to it. Ask a question in plain language, get an answer from your real records. Tell Claude to save a meeting note, update a contact, log a decision, it writes directly to the database.

The key design choice: Claude only accesses your data when you’re talking to it. You ask, it answers. You instruct, it writes.

You get the full power of a frontier AI model working with your complete business data, but you stay in the loop for every interaction. For business data, I believe that’s the right tradeoff. Maybe not forever. But right now, absolutely.

What this means practically

I just started using the chat as the universal interface to my CRM, spreadsheets, folders, and everything else.

A week in, you stop pasting context into prompts. A month in, you stop opening half the tools you used to switch between. The database becomes your operating layer. Claude becomes the interface. Claude by itself is already a great tool, it was just missing the context. Now it’s not.

That’s the system. Local, private, controlled, and connected to real data through Hexomatic pipelines that keep it current.

Second Brain launches April 20. Secure your license now at secondbrain.hexact.io.

If you already have a Hexomatic account, you get a free bonus session showing how to connect your scraping workflows directly to your Second Brain.

So here it is.

Second Brain is now available.

I want to be clear about what happened here, because the backstory matters.

I did not set out to build a product. I set out to fix a problem that was driving me crazy. I run three companies. Hexact, with thousands of users and years of customer data. Grillyan, a service business in South Florida with over a thousand clients. Urgify, with products still in early stages. Every time I opened Claude, I had to re-explain which company I was asking about, paste in context, hope it didn’t confuse one business with another.

I wrote about this problem over a year ago. Then again six months later. Each time I was a step closer but the system wasn’t ready for anyone else to use. The schema kept changing. The import pipeline was fragile. The dashboard was half-built.

Now it’s done. I use it every single day. It holds all my contacts, articles, customer records, payment data, meeting notes, hiring information, and documents across all three businesses. Claude connects to it directly and answers from real data.

What this actually is

Second Brain is a local application that runs on your computer. Not a cloud service. Not a subscription. A database and a web dashboard that live on your machine, with a direct connection to Claude through MCP.

You get:

• A local knowledge base with a browser-based dashboard to browse, search, filter, and manage your data

• A personal CRM with contacts, emails, organizations, relationships, and spend tracking

• Import tools for CSV, ZIP, and platform exports that handle deduplication automatically

• A direct connection to Claude so you can ask questions in plain language and get answers from your actual records

• Email deliverability tracking to build clean contact lists

Everything is local. Your data never leaves your machine. You own it completely. There is no recurring payment.

Why I am releasing this now

I’ll be honest. I could have kept this private and just used it myself.

But the replies to my last article made something obvious. The people reading this newsletter are exactly the people who need this. You work with data. You use AI daily. You’re frustrated by how much time you waste re-explaining context. And most of you have tried some version of this, whether it’s a giant Notion database, a folder full of CSVs, or custom GPTs stuffed with instructions, and it didn’t work the way you needed it to.

I also wrote last year about how AI doesn’t actually understand your business. It processes text. It predicts responses. The only way to make it useful for real business questions is to give it structured, queryable data. Not pasted paragraphs. Not memory notes. Actual records it can filter, count, compare, and reason over.

That article was the theory. Second Brain is the implementation.

Three ways to get it

I set up three options based on how much help you need with installation.

Self-setup license — you get the application and a setup guide. Install at your own pace.

Guided setup license — you get the application plus a live group session on April 20 where I walk through the full installation on screen and answer questions in real-time.

Done-for-you license — you get the application plus a private 1-on-1 session where I help you to install it on your machine and load your actual business data.

Early pricing is available right now through April 11.

→ Get your license at secondbrain.hexact.io

The application releases on Monday, April 20. All buyers receive access on that date.

If you already use Hexomatic

If you have a paid Hexomatic account, you already own a tool for keeping a second brain alive. Hexomatic can scrape competitor pricing pages, pull Google Maps data, collect job postings, extract product listings, and output all of it as structured CSV files. Those files go directly into your Second Brain through the import pipeline.

Every Second Brain buyer who has a Hexomatic account gets free access to a bonus session on April 23 showing exactly how to connect the two. Your AI stays current without you doing manual research.

I described this pipeline in detail in my last article. The difference is now you can set it up yourself instead of reading about mine.

One more thing

I’ve spent the last year writing about slow automation, about the gap between AI hype and reality, about why rushing into AI usually backfires. I still believe all of that. Second Brain is not a magic fix. It’s a tool that requires you to think about what data matters to your business and commit to keeping it current.

But if you do that work, the compound effect is real. Every record you add today makes every AI conversation smarter tomorrow. That’s not a marketing line. That’s what I’ve been experiencing for months, and it changed how I make decisions across all three of my companies.

→ secondbrain.hexact.io

This is the article I wanted to read before I built my own system. So I wrote it.

MCP, which stands for Model Context Protocol, was introduced by Anthropic in late 2024 as an open standard for connecting AI models to external data sources. By early 2025, OpenAI, Google DeepMind, and hundreds of developer tools had adopted it. The protocol addresses what Anthropic described as an “N×M” data integration problem, where every AI tool previously needed a custom connector built for every data source. Today it is the standard way to give AI real-time access to databases, files, and business systems without custom code for each connection.

The reason this matters is not technical. It is practical. Claude and ChatGPT are not blind to who you are. Both have memory features. AI remembers things across conversations. You can upload files, paste context, and write custom instructions. That works for simple, one-off questions.

It breaks the moment your questions get specific.

There is a real difference between an AI that remembers “I run a SaaS company” and an AI that can answer “which customers signed up last month, have not logged in since, and are still on a paid plan?” The first is a memory note. The second requires structured, queryable data. No amount of pasted context or memory snippets gets you there.

Now multiply that across more than one business. If you run multiple companies, each with its own customers, competitors, products, pricing, and market signals, asking AI to hold all of that coherently in a conversation becomes unreliable fast. Competitor pricing updates, new customers entering your CRM, fresh newsletter articles, recent reviews, changes in your local market: none of that exists in AI’s training data, and none of it fits cleanly in a context window alongside everything else.

The fix I built: a local SQLite database connected to Claude via an MCP server, updated daily by automated scraping workflows in Hexomatic and synced with our CRM database. When I ask Claude something about my business now, it queries real, current, structured records and returns a precise answer built from actual data.

Here is exactly how it works.

The Real Gap: Memory vs. Queryable Data

People assume the solution is better prompts or more context pasted in. It is not.

Pasted context works for one-off questions with small datasets. It breaks on anything requiring counting, filtering, comparing across time, or joining information from two sources. The moment your question sounds like a database query, you need a database.

AI is very good at reasoning over structured data once it has access to that data through a proper connection. It is not a replacement for the data itself. That distinction matters more than any other part of this setup.

The Architecture: Three Components

The system has four parts. Each one does a specific job.

1. A local SQLite database

SQLite is a file-based database. No server, no cloud, no subscription. It runs on your machine. You can open it in any database viewer, query it with standard SQL, and extend it as you need. I use it to store everything: my published articles, my customers, my contacts, my product catalogue, competitor data, and more.

The structure matters less than the habit of keeping it current.

2. An MCP server

MCP stands for Model Context Protocol. It is an open standard that lets Claude connect to external tools and data sources directly inside a conversation. Instead of copy-pasting data into a prompt, you expose your database through an MCP server and Claude queries it on its own.

Setting up an MCP server is a technical step, but it is not complex, just ask Claude how to do it.

3. Hexomatic as the daily data pipeline

This is the part that keeps the external data alive. A static database goes stale within weeks. Competitors change their pricing. New articles get published. Tenders open and close. Local market listings shift.

Hexomatic runs scheduled workflows that scrape specific sources and outputs the results as CSV files. Those files then need to make their way into your local SQLite database. I built a small local import app that watches a folder on my machine. When a new CSV lands there, it picks it up and inserts the records automatically.

The one manual step is downloading the CSV from Hexomatic once a day. That sounds like it defeats the purpose, but I made that choice deliberately. Connecting an automated pipeline directly from the web into your local machine is a security tradeoff I am not comfortable with yet. One manual click per day keeps the data flow air-gapped from any external system. The database stays local, the connection to Claude stays local, and nothing from the web writes directly to my machine without me seeing it first.

This is a part of the setup we are actively thinking about. There will likely be a cleaner solution that handles the import automatically without compromising the security model. For now, one download per day is a reasonable tradeoff given what the rest of the system gives you in return.

4. CRM sync

External data from the web is only half the picture. The other half is your own operational data: customers, signups, payments, job history, service records, support tickets. This lives in your CRM, and it needs to flow into the same SQLite database so Claude can reason across both layers at once.

This is the part most people skip. They connect AI to web data and stop there. But the most useful questions are the ones that cross both layers: which customers in a specific region have not been contacted since a competitor changed their pricing in that area, or which clients are due for a follow-up based on their last service date. Those questions only work when external signals and internal records live in the same place.

What to Scrape and Insert

The specific data depends on your business. Here is what I track, and why each one matters.

Your own published content

Every article, newsletter, or post goes into the database as soon as it is published. When I ask Claude to suggest a new article angle, it already knows everything I have written. It does not suggest topics I covered two years ago.

Competitor pages

Pricing pages, feature lists, landing pages, blog content. Scraped weekly. When a competitor adds a new plan or removes a feature, it appears in the database within days. Claude can tell me exactly what changed and what it signals.

Customer data from your own tools

That data flows into the database automatically. When I ask Claude about customer behavior or revenue trends, it reads from actual records.

Google Maps and local listings for service businesses

For my local service business, I track competitors in South Florida. New businesses entering the market, existing ones closing, review counts shifting. Hexomatic scrapes this weekly. AI can give me a competitive briefing on the local market without me doing any manual research.

Tender and procurement portals

If your business responds to bids or contracts, scraping relevant procurement portals and inserting new listings into the database turns AI into a bid discovery assistant. Ask it for qualified opportunities this week, and it pulls from real current data.

Industry news and job postings

Competitor hiring patterns reveal what they are building before they announce it. A company adding a machine learning team means a product shift is coming. Scraping job boards and inserting the results gives AI a forward-looking signal that would otherwise require hours of manual tracking.

What AI Can Do With This Data

The difference is not subtle. Here are examples of questions I ask that would be impossible to answer properly without the database.

“Which customers have not logged in for 90 days but are still on a paid plan?” Claude queries the database and returns a list I can act on immediately.

“What topics have I written about most in the last six months, and what gaps exist based on what customers are asking?” Claude cross-references my article archive against the support tickets and returns a content gap analysis.

None of this requires a prompt full of pasted data. Claude already has the context. The conversation can stay focused on the decision, not on the setup.

The Part That Most People Skip

The database alone is not enough. The workflow that keeps it updated is what makes the system worth building.

The Hexomatic workflows run on a fixed schedule. Some are daily. Competitor pricing pages, new customer signups, recent articles. Some are weekly. Google Maps competitor data, tender portals, job listings. Some are monthly. A broader competitive landscape sweep and a full content audit.

The schedule is not the point. The habit is. Once the workflows are running, the database stays current without you touching it. AI always has something real to work with.

How to Start

You do not need to build all of this at once. Start with one data source that would immediately change how you use AI.

If you are a content creator, start by inserting your published articles. AI will stop suggesting duplicates and start giving you genuinely useful feedback on gaps.

If you run a SaaS or subscription business, start with your customer data. The ability to ask natural language questions against real customer records is immediately useful.

If you compete in a local market, start with Google Maps data for your area. A weekly scrape and a simple table in SQLite gives AI enough context to brief you on competitive shifts without any manual research.

Build one workflow. Let it run for two weeks. Then extend it.

The underlying point is simple. AI is not going to learn your business on its own. But once you give it the right data, it stops guessing and starts being useful in a way that actually compounds over time. The information going in today makes the answers better next month, and better still the month after.

That is the system worth building.

I am just getting close to the point where this setup delivers the real benefit I had in mind when I started building it. More to come, including updates on how Hexomatic fits deeper into this pipeline as we develop it further. The broader topic of building a second brain, outside of Hexact products and from a more personal angle, is something I cover in my personal newsletter. If that wider context interests you, follow along at publication.aslanyan.net.

If you want to build the Hexomatic workflows that power this kind of setup, start with Hexomatic’s Google Search and Get page content automations. If you would rather have it built for you, the concierge service is the fastest path: calendly.com/hexact/concierge-service-hexact

For years that was the only reliable way to extract structured data.

In many cases, you can now skip that entire step using Hexomatic.

The Problem With Scraping Templates

Scraping templates are powerful, but they carry a real cost. Every website has a different structure, every template has to be built manually, and every layout change can break the extraction.

If you’re analyzing 2 to 5 websites, that’s manageable. If you’re researching hundreds, it turns into maintenance work.

The goal of scraping is not building templates. The goal is getting useful data quickly.

The Shortcut Most People Miss

Instead of building a scraping template for every page, you can do something much simpler inside Hexomatic: extract the entire page content first.

That’s exactly what the Get Page Content automation does. It loads the page and returns the main readable content, the same information a human sees when reading it: headings, paragraphs, product descriptions, company details, speaker bios, job listings, article text.

No selectors. No HTML mapping. No template building.

Then Let AI Structure the Information

Once you have the page content, download the CSV and pass that to any AI tool you already use. ChatGPT, Claude, Gemini, whatever model you prefer. Ask it to normalize the information.

Example prompt:

Extract all speakers from this file and return: First Name, Last Name, Company, Role.

The AI reads the content and returns structured data. Modern language models are good at detecting names, identifying companies, understanding context, and restructuring messy text. Instead of manually defining the page structure, you let the model interpret the content.

Why This Is Faster

This approach removes the most time-consuming part of scraping: template engineering.

You no longer need to inspect HTML, locate selectors, rebuild templates for each site, or maintain them when layouts change. You extract the content with Hexomatic, then normalize it with AI. For research tasks, this is often dramatically faster.

When Scraping Templates Still Make Sense

Templates are still the right choice for large structured scraping jobs: pulling thousands of products from e-commerce sites, collecting catalogs with prices, SKUs, and inventory, or monitoring datasets where fields must always match the same structure. In those situations, a scraping template extracts exactly what you need and runs reliably at scale.

The rule is straightforward. Heavy structured scraping: use templates. Research, discovery, and content extraction: use Get Page Content automation + AI.

The Practical Workflow

A simple workflow many teams now use:

1. Run Get Page Content in Hexomatic. Extract the readable content from each page.

2. Send the content to your AI tool. Use your preferred model to pull the fields you need.

3. Normalize the output. Return structured data like names, companies, roles, locations, product features, or pricing information.

No custom scraping templates required.

Still Need a Custom Scraping Template?

If your use case requires a structured template at scale, and you’d rather not build it yourself, that’s what the Hexomatic Concierge Service is for. Share your requirements and we’ll set it up for you.

Order Concierge Setup →

Not sure which approach fits your data problem? Book a free 15-minute call. Walk us through what you’re trying to extract, and we’ll tell you exactly what you need.

That was the entry point. This is the next level.

The shift is simple: you stop thinking in terms of search queries and start thinking in terms of data systems.

Google is not where you search. It is where you query fragmented databases. Your job is to structure them, and Hexomatic is what makes that repeatable.

The Real Limitation Is Not Google. It’s Your Query Design.

Most people write queries like this:

“plumber miami”

That is not a data query. That is browsing.

A real data query has three components:

1. Entity — who or what you are looking for (plumber, school principal, supplier, property manager)

2. Context — where that entity exists (city, industry, domain, document type)

3. Signal — what proves the data exists (email, directory, file, registration, contact page)

Example:

"property manager" "miami" "email" site:.org

Now Google is not guessing. It is filtering. And when you feed that into Hexomatic’s Google Search Scraper, you are not browsing results, you are extracting a structured dataset from them automatically.

Build Query Sets, Not Queries

One query gives you results. A query set gives you coverage.

Instead of:

"restaurant owner miami email"

You run:

"restaurant owner" "miami" "email"
"restaurant group" "miami" "contact"
"hospitality management" "miami" "team"
"food service director" "miami"
intitle:"restaurant group" "miami"

Each query hits a different surface: directories, team pages, PDFs, press mentions, listings.

In Hexomatic, you paste all of these as a single input list into the Google Search Scraper. One workflow runs all five angles in parallel, pulling URLs across every surface simultaneously. No manual tabbing between results, no copy-pasting into spreadsheets.

The Hidden Layer Most People Ignore: Documents

Web pages are optimized for SEO. Documents are optimized for internal use. That makes documents better.

Try this:

filetype:pdf "vendor list" "florida"
filetype:xls "supplier" "miami"
filetype:csv "contact" "department"

What you get: internal spreadsheets, procurement lists, structured contact data, zero design, pure information.

This is cleaner than scraping websites.

Stop Extracting Pages. Start Extracting Patterns.

This is where most workflows stay basic. They scrape URLs, extract text, export CSV. That is not enough.

You want to extract patterns across pages.

Say you scrape 200 “team” pages. Instead of pulling raw text, you want to identify roles (CEO, manager, director), extract emails, map company to people to roles, and classify seniority.

Here is how that looks in Hexomatic:

1. Google Search Scraper pulls the URLs

2. Website Crawler maps all pages within each domain

3. Page Content Extractor pulls visible text from team and contact pages

4. Email Scraper extracts addresses

5. AI classifies each contact: “Is this a decision-maker level role based on the following text? Return yes or no with the job title.”

Now you have structured intelligence, not a pile of text. The AI block here is not writing copy. It is doing classification at scale across hundreds of records without you touching a single one manually.

Layering: The Difference Between Data and Insight

Basic workflow:

Google → URLs → extract data → export

Advanced workflow:

Google → URLs → extract data → enrich → classify → filter → export

Hexomatic handles every step in that chain natively. The Google Search Scraper feeds the Page Content Extractor, which feeds the AI, which outputs to Google Sheets or CSV, all inside one workflow you run once and then schedule.

The result: you are not collecting data. You are pre-qualifying it before you even look at it.

Time Is the Real Constraint, Not Data

Scraping is not slow because of the tool. It is slow because of the web. Websites rate-limit requests, slow down responses, and block aggressive traffic. If you force speed, you lose access.

Hexomatic runs everything in the cloud and manages request pacing automatically. That is why some runs finish in minutes and others take a few hours. You are not waiting on your machine; the system is working around the web’s constraints while you do something else.

What you can control: how clean your queries are. Better queries produce less noise and faster useful output.

Real Advanced Use Cases

1. Supplier Intelligence

filetype:pdf "approved vendors"
filetype:xls "supplier list"
"vendor registration" "construction"

Output: real supplier networks and procurement access points pulled directly from internal documents.

2. Hidden Decision-Makers

intitle:"team" "company name"
"operations manager" "city"
"facility manager" "contact"

Use Page Content Extractor, then AI to classify seniority. Output: actual people with roles, not generic contact forms.

3. Content and SEO Gaps

"how to" "industry keyword"
intitle:"guide" "keyword"
inurl:blog "keyword"

Scrape titles and page content with Page Content Extractor, then use AI to cluster topics and surface gaps. Output: a mapped content landscape across your entire niche.

The Shift That Matters

Beginners use Google to find pages. Operators use Google to extract data. Advanced users use Google to build repeatable data systems that run automatically.

Hexomatic is the layer that makes it scalable, from query design to scheduled pipeline to clean CSV or Google Sheets output, without writing a line of code.

If You Missed the Basics

Start here first: Unlock Hidden Data with Google + Hexomatic (No APIs Needed)

Then come back to this one.

What to Do Next

Take one niche. Write 10 query variations and run them with Hexomatic’s Google Search Scraper. Add the Page Content Extractor and Email Scraper.

Run it once. That is enough to see what this replaces.

Start building on Hexomatic

Prefer to skip the setup entirely? The Hexomatic Concierge Service will build the workflow for you.. For larger-scale or ongoing needs, book a call and we will scope a custom solution.

Both articles got more traction than I expected. Hundreds of readers set up the workflows. And the same question kept showing up in my inbox, across email replies, and in the comments.

“I have the system running. I’m pulling results every week. But my competitors still seem to find opportunities I never see. What am I missing?”

That question points to a real gap. And this article answers it.

The System I Described Works, Up to a Point

If you followed the workflow from those articles, you’re already ahead of most people in your space. You have AI-generated keyword queries, a Hexomatic workflow scraping Google results on a schedule, and ChatGPT filtering the output into a qualified shortlist. For many industries that surfaces hundreds of relevant opportunities every week.

But here’s what that system doesn’t solve.

Google is only the discovery layer. It is not the source of most tenders.

The majority of procurement opportunities originate inside systems Google barely touches: government procurement portals, university vendor platforms, corporate supplier systems, industry-specific aggregators. Many of these run on internal databases with dynamic search tools. Google may index the homepage, but it rarely indexes the individual listings inside. A perfect keyword strategy still misses a significant portion of the market.

In practice, Google is useful for finding where procurement happens. After that, it becomes a middleman you don’t need.

Turn Each Portal Into a Permanent Data Stream

Once you identify a procurement portal with relevant opportunities, there’s no reason to keep running Google searches to rediscover it. The smarter move is to monitor it directly.

Here’s what that looks like. You find a city procurement portal publishing active bids in your category. Instead of visiting it manually each week, you build a Hexomatic workflow that extracts the tender title, deadline, department, reference ID, and link to documentation on a fixed schedule. Every new opportunity from that portal lands in your dataset automatically.

You own the data stream. You no longer depend on Google to surface it.

That shift, from searching to monitoring, is the difference between finding tenders reactively and running an actual intelligence system.

Where Most Teams Get Stuck

At this point a lot of people try to build these scrapers themselves. They hit the reality of procurement portals fast.

Results only appear after selecting filters from a dropdown. Pages load dynamically with no stable URL structure. Links change every session. Key details are buried inside attached PDFs. Pagination runs on JavaScript rather than clean URL parameters.

Standard scraping tools break on all of these. And this is where most teams give up, not because the opportunities are inaccessible, but because building a reliable workflow on top of a messy portal requires experience most people don’t have.

This is exactly the problem Hexomatic was built to solve.

What Hexomatic Handles That Manual Search Cannot

With Hexomatic you can pull Google results at scale with the Google Search Scraper (as covered in the first article), extract structured data from listing pages, follow links automatically to pull full solicitation details, extract text from PDFs and attached documents, and run AI classification across the full result set to score and filter before you ever open a spreadsheet.

Once scheduled, the system runs without you. Instead of spending Monday mornings trawling procurement sites, you open a ready-made shortlist of qualified opportunities.

The time saving is real, but it’s not the main value. Coverage is.

A Faster Way to Start: Hexowatch for Simple Portals

Not every portal needs a full Hexomatic scraping workflow right away. Some procurement pages are simple enough that the fastest approach is to watch them for changes first, then build a structured scraper once you’ve confirmed the portal is actually worth monitoring.

This is where Hexowatch fits.

Hexowatch monitors the webpage for changes and alerts you when something updates. For tender monitoring, that means you can point it at a procurement portal’s listings page and get notified the moment new opportunities appear, without configuring extraction fields, output formats, or workflow logic.

Practical scenarios where Hexowatch makes sense:

You’ve identified a portal but aren’t sure how active it is. Instead of spending time building a full recipe, set up a Hexowatch monitor. If it fires alerts regularly, the portal is worth a proper Hexomatic workflow. If it barely changes, you’ve saved yourself an afternoon.

The page structure is too simple to justify a full workflow. A single-page listing with 5 to 10 active tenders at a time doesn’t need structured extraction. A Hexowatch alert tells you when to go check it manually, which takes two minutes.

You want same-day alerts, not weekly batch exports. Hexomatic workflows are designed for scheduled bulk collection. Hexowatch is designed for real-time change detection. For time-sensitive categories where a tender can open and close within days, being alerted the same day it’s posted matters.

Think of Hexowatch as the lightweight layer. It tells you something changed. Hexomatic tells you exactly what changed and structures it for you. For most serious procurement pipelines you’ll end up using both: Hexowatch for fast alerts on known portals, Hexomatic for structured data extraction at scale.

The Opportunities That Only Show Up If You’re Watching

A lot of the most actionable tenders are published in places that never surface in Google. Small municipal portals. University procurement systems with obscure vendor registration pages. NGO project portals. Industry-specific platforms that don’t invest in SEO.

Competitors running keyword searches miss all of these. Competitors monitoring the portals directly capture them.

The longer your monitoring system runs, the more the advantage compounds. After a few months you start seeing which agencies publish contracts on a recurring cycle, which departments consistently buy what you offer, which months drive the highest procurement volume in your sector. At that point you’re not just finding tenders. You’re anticipating them.

When You Need a Custom Setup

Some portals are straightforward to scrape. Clean pagination, static URLs, visible listing data. Hexomatic’s built-in recipe builder handles these without much setup, and if you followed the second article, you already know how to configure a basic recipe.

Others require a custom approach. If a portal uses a login before listings are visible, session-based URLs that break between visits, dropdown-gated results, or embedded documents that need text extraction to pull bid details, a standard recipe won’t hold up reliably. You’ll spend hours getting it to work and it’ll break the next time the portal updates.

Our concierge team builds these custom scrapers regularly. Once configured, the workflow runs on schedule and delivers structured tender data every week with no maintenance on your end. If you know a specific portal that consistently publishes in your niche, book your concierge service here.

The Right Way to Think About This System

Most teams approach tender discovery backwards. They start with manual search, automate one piece at a time, and end up with a half-working system that still demands hours of weekly effort.

The better framing is to treat the entire process as a data pipeline with the right tool at each layer.

Google + Hexomatic to discover and collect from procurement sources at scale.

Hexomatic keyword refinement + direct portal recipes to go deeper than Google can reach.

Hexowatch for real-time change alerts on known portals, so you’re notified the same day something new is posted.

Hexomatic structured extraction + AI filtering to turn raw listings into a scored, qualified shortlist you can act on.

Once those layers are running together, new opportunities appear continuously without you searching for them.

If you want to build this but don’t want to spend your time on setup, book a concierge call.

A real digital profile audit works differently. It pulls dozens of data points across social platforms, news mentions, business directories, forums, and review sites, then uses AI to analyze what’s actually there, not guess. Here’s how to do it systematically using Hexomatic to scrape Google at scale and AI to make sense of the results.

This works for researching yourself, a competitor, a potential hire, a business partner, or any company you’re about to sign a contract with.

Step 1: Generate Your Keyword List with AI

Before scraping anything, you need a comprehensive list of search queries. The goal is to cover every angle: name variations, professional mentions, social profiles, news, reviews, and associations.

Use this prompt in Claude or ChatGPT:

Prompt: Generate Research Keywords for a Person

My name is [Name]. Generate a comprehensive list of Google search queries to find any publicly available information about me online. The list should progress from broad to specific, and cover:

1. Basic name searches (full name, name + location, name + profession)

2. Professional and business mentions (name + company, name + CEO/founder/role)

3. Social media profile searches using site: operators (LinkedIn, X, Facebook, Instagram, YouTube, TikTok, Reddit, GitHub, Pinterest)

4. News and media mentions (name + interview, name + article, name + podcast)

5. Review and forum mentions (name + review, name + Quora, name + Reddit)

6. Business and directory listings (name + Crunchbase, name + AngelList, name + Bloomberg)

7. Academic or publication mentions if applicable

8. Image and video searches

Format the output as a plain list of search queries, one per line. Start with broad queries and end with site-specific ones. Example format:

John Doe
John Doe CEO
John Doe [company name]
John Doe interview
John Doe podcast
John Doe review
John Doe Crunchbase
site:linkedin.com John Doe
site:twitter.com John Doe
site:instagram.com John Doe
site:facebook.com John Doe
site:reddit.com John Doe
site:github.com John Doe
site:youtube.com John Doe
site:crunchbase.com John Doe

Generate at least 40 queries. Be exhaustive.

This produces a ready-to-use keyword list. Copy it into a CSV with one column labeled keyword. That’s your input file for Hexomatic.

Step 2: Build the Hexomatic Workflow to Scrape Google

In Hexomatic, create a new workflow with the following setup:

Start with Data Input: Upload your CSV of keywords, one per row.

Automation 1: Scrape from Google Search Use the “Scrape Google Search Results” automation. Set it to pull maximum (700) results per keyword. This returns the URL, page title, and description for each result.

Automation 2 (optional): Page Content Extractor If you want deeper data, chain a “Page Content Extractor” step to pull the visible text from each result URL. Useful for articles, interviews, or directory pages that mention the person or company in detail. (or do this after filtering the irrelevant results)

Output: Export to Google Sheets or CSV. You’ll get a spreadsheet with columns like: keyword, result URL, title, snippet.

Run the workflow once for a one-time audit, or schedule it to run weekly if you want to monitor how a profile evolves over time.

Step 3: Manual Filtering

Once you download the results, go through them quickly. You’re looking for two things to remove:

• Results that belong to a different person or company with the same name. This is common with generic names or popular brand terms.

• Irrelevant aggregator pages, placeholder results, or spam directories with no real content.

Firstly remove the duplicate results, then add a “relevant” column to your spreadsheet. Mark each row Y or N, then filter to only the Y rows. This takes 10 to 20 minutes depending on volume and prevents the AI from analyzing noise in the next step.

Step 4: AI Profile Analysis

Take your cleaned list of URLs, titles, and snippets (and probably content of the pages) and run them through this prompt:

Prompt: Analyze a Person’s Digital Footprint

Below is a list of Google search results gathered about a person named [Name]. Each row contains a URL, page title, and a short snippet.

Important: Base all conclusions strictly on the data provided. Do not infer, assume, or fill gaps with information not present in the dataset. If something is unclear or absent, say so explicitly.

Analyze this data and produce a structured digital profile report covering:

1. Professional identity — What roles, titles, or companies are they associated with? What industry are they in?

2. Online presence strength — Which platforms do they appear on? How active do they seem based on the data?

3. Public reputation signals — Are there any reviews, testimonials, or public opinions? Positive or negative?

4. Media and content footprint — Have they been interviewed, written articles, appeared on podcasts, or published content?

5. Business associations — Are there any companies, products, or brands linked to them?

6. Gaps and blind spots — What’s missing? Where is there no presence that you’d expect based on what is present?

7. Key talking points and themes — What topics or ideas keep coming up across sources?

8. Pattern analysis — Assess the consistency of their messaging across platforms, personal brand coherence, and narrative strength. Does the same story come through everywhere, or are there contradictions and gaps?

9. Risk signals — Are there any mentions of legal issues, controversies, or reputational risks?

At the end, summarize the overall digital profile in 3 to 5 sentences as if you were briefing someone before a business meeting with this person.

Here is the data: [PASTE YOUR CLEANED CSV ROWS HERE]

The output gives you a structured picture of how someone appears online. The anti-hallucination instruction at the top is important: without it, AI models will fill gaps with plausible-sounding guesses, which defeats the purpose of a data-driven audit. You want conclusions drawn from what’s actually there, not from what the model thinks is likely.

The Same Process Works for Companies

The workflow is identical for company research. Swap the person’s name for the company name and expand the keyword list to include reputation-specific queries:

• Acme Corp

• Acme Corp reviews

• Acme Corp complaints

• Acme Corp vs [competitor]

• Acme Corp pricing

• site:trustpilot.com Acme Corp

• site:glassdoor.com Acme Corp

• site:reddit.com Acme Corp

• site:bbb.org Acme Corp

• site:g2.com Acme Corp

• site:producthunt.com Acme Corp

This turns the workflow into a reputation intelligence audit. You get a clear picture of how customers, employees, and the broader internet talk about a company before you sign a contract, consider a partnership, or go up against them competitively.

Use the same AI analysis prompt. Just replace the person-specific framing with company framing in your briefing request at the end.

What This Is Actually Useful For

• Founders and executives auditing their own presence before a fundraise or media push

• Sales and BD teams researching decision-makers before outreach

• Journalists and researchers building background on a subject

• HR and hiring teams doing pre-offer due diligence

• Anyone who wants to know what the internet actually says about them before an investor or journalist does

The whole process from keyword generation to AI report takes about an hour. Most of that time is the manual filtering step, which you can’t fully automate if accuracy matters. Everything else runs in the background while you do something else.

Inspect elements.

Define selectors.

Test.

Adjust.

Fix when layout changes.

Custom scrapers are powerful. In many cases they are absolutely necessary, especially when you need structured fields from specific page elements.

But there are many situations where that level of precision is not required.

When researching a company, analyzing a competitor, or mapping a topic, you often just need the readable content.

That is exactly where the Get Page Content automation becomes a practical shortcut, it pulls the clean, human-readable text from any page so you can move straight to analysis instead of building extraction rules.

The Built-In Shortcut: “Get Page Content”

Hexomatic includes a built-in automation called Get Page Content.

https://hexomatic.com/automation/get-page-content

It extracts all readable text from a web page.

Titles.

Subtitles.

Paragraphs.

Lists.

Table text.

Button labels.

Navigation text.

Footer content.

Literally every piece of text that is visible and readable on the page.

It does not try to guess fields.

It does not depend on CSS selectors.

It does not break when a div changes position.

You simply:

Provide a URL.

Run Get Page Content.

Export the cleaned text.

No custom extraction logic.

No ongoing maintenance when the layout changes.

When This Approach Makes More Sense

Custom scrapers are ideal when you need structured tables, prices in specific fields, or repeated product blocks.

But if your goal is:

• Understanding positioning

• Extracting messaging themes

• Comparing tone of voice

• Analyzing service structure

• Mapping content topics

You do not need structured HTML fields first.

You need content first.

AI can handle the structuring.

Analyze an Entire Company in Minutes

Here are two simple workflows.

Option 1: Crawl First, Then Extract

Hexomatic includes a crawler.

1. Enter the domain.

2. Collect internal URLs.

3. Export the list.

4. Run Get Page Content on those URLs.

You now have the full readable footprint of the company.

Main pages.

Subpages.

Blog.

Legal.

Resources.

All in clean text form.

Option 2: Use the Google Search Scraper

If you want more control over which pages are included:

1. Use the Google Search scraper.

2. Apply operators like

   site:domain.com

   or specific topic keywords.

3. Export the URLs.

4. Run Get Page Content.

This works for:

• Multi-company comparisons

• Industry research

• Niche content mapping

• Trend tracking

Let AI Do the Structuring

Once you have the readable text, the next step becomes simple.

Send it to your preferred AI model and ask:

• Summarize the company strategy

• Extract core services

• Identify differentiators

• Detect pricing models

• Categorize themes

• Generate competitor comparisons

AI models are very effective at analyzing large blocks of text.

Instead of investing time maintaining extraction logic, you move directly to insight.

A Practical Rule of Thumb

If you need exact fields, build a custom scraper.

If you need understanding, start with content.

That small shift often saves hours.

You might say, any AI tool can summarize a website anyway. Yes and no. AI can summarize what you give it. The difference here is that you control the exact source data. You extract the full current text directly from the live page, then run your own prompts and analysis on that precise dataset. No guessing, no partial context, no outdated cache.

If you prefer that we build the workflow for you, including crawling, scraping, and AI analysis, request our concierge service: https://calendly.com/hexact/concierge-service-hexact

1. What is Hexowatch actually built for?

Hexowatch monitors changes on websites.

Not traffic.

Not rankings.

Not analytics.

It tracks specific page-level changes and alerts you when something changes.

Typical use cases:

• Competitor price changes

• Content edits on landing pages

• New job postings

• Legal or policy updates

• Product availability

• Visual layout changes

• Lost backlinks detected

• Changes in technology stack

• Domain ownership or Whois record changes

• New pages added to sitemap

• API response changes

• Keyword additions or removals on a page

It runs on defined monitoring types, such as:

• HTML element monitoring

• Visual monitoring

• Keyword monitoring

• API monitoring

• Technology stack changes

• Whois records

• Backlink monitoring

• Sitemap monitoring

The key point: Hexowatch monitors page structure and content, not opinions or interpretations.

2. How reliable is it?

Reliability depends on two things:

1. The monitoring type you select

2. The stability of the target page structure

If you use:

• HTML element monitoring on a stable page structure, reliability is very high.

• Visual monitoring on dynamic pages, false positives will occur.

When the underlying DOM structure changes, even if the page looks visually the same, an HTML-based monitor can break.

That is not a bug. It is how the web works. Also, there are many cases when the website loads differently in the emulated browser rather than your regular browser. This is also something that we do not control, but it usually happens with old websites or ones that do not optimize for different screen sizes.

If the page structure changes, the monitor must be rebuilt.

3. Why did my monitor stop working?

This is one of the most common questions.

It usually happens because:

• The website changed its structure

• The website added bot protection

• The website server timed out

• The website started rate limiting

Website changes do not matter. What matters is the exact page structure used when the monitor was created.

If that structure changes, the template must be recreated.

4. When are premium credits required?

Premium credits are needed only in specific cases:

• When residential proxies are required, otherwise the server blocks bot access

• When you want the website to be loaded as in a specific region

Standard monitoring runs without premium credits.

5. Does Hexowatch bypass website protections?

No system can universally bypass all protection.

Some websites use:

• Cloudflare protection

• CAPTCHA systems

• Rate limiting

• Geo restrictions

• Login requirements

In such cases, additional configuration may be required, including residential proxies, a full stack browser, or enabling the Hexowatch user agent if you control the website.

Even then, access is not guaranteed. Only website owners control access to their infrastructure.

6. Can Hexowatch monitor password-protected pages?

Yes, but only when properly configured.

You can monitor:

• Pages behind login

• Private dashboards

• Member-only content

However, session expiration, 2FA, or login changes can break monitors.

If a site requires constant manual verification, it may not be suitable for automated monitoring.

7. How often can it check pages?

Monitoring frequency depends on your plan.

Higher-tier plans allow more frequent checks.

However, increasing frequency does not always improve results. Some sites rate limit aggressive checking.

Monitoring every 15 minutes on a fragile site can lead to blocks.

The smarter approach is matching frequency to real business need.

8. What is the difference between Visual and HTML element monitoring?

HTML element monitoring:

• Tracks specific elements

• Very precise

• Breaks if structure changes

Visual monitoring:

• Tracks screenshots

• Can trigger alerts for minor visual shifts

Use HTML element when you need precision.

Use Visual when you need to track any pixel on the page.

9. Can Hexowatch scrape data?

Hexowatch is not primarily a scraping tool.

For heavy data extraction workflows, Hexomatic is more appropriate.

Hexowatch focuses on change detection and monitoring.

10. Why am I getting false positives?

Common causes:

• Dynamic timestamps

• Rotating banners

• Ads

• Session-based elements

• Minor CSS shifts

Web pages are not static documents. Many are dynamically rendered with JavaScript.

Reducing false positives requires selecting stable elements and ignoring dynamic regions.

Hexowatch works extremely well when:

• The right monitoring type is chosen

• The target page structure is stable

• Expectations align with how websites actually function

It is not magic.

It does not override site security.

It does not prevent websites from changing.

It monitors defined page structures and alerts when those structures change.

That is the job.

Concierge and Custom Support

If you want help creating monitors correctly from the beginning, auditing your current setup, or maintaining templates when websites change, we offer concierge service and custom paid support.

You can book a one-time Concierge Session here:

https://calendly.com/hexact/concierge-service-hexact