How to Do a Comprehensive Person or Company Research Using Hexomatic and AI

Most people research themselves or someone else the same way: a quick Google search, maybe a follow-up question in ChatGPT, and they call it done. The problem with Google is you only see the first page. The problem with ChatGPT is it either doesn’t know the person, makes things up, or gives you outdated information it was trained on.

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.