Four years ago today, I moved to the U.S.
I’ve written about Second Brain (Hexact) a lot and mentioned my local service business along the way. I’ve never written about how one led to the other.
Why a service business
I quickly realized I couldn’t rely on a software startup alone. I needed something on the ground, next to Hexact.
Software is still my main business. But with AI it moves faster every month. What was relevant a year ago can be irrelevant today. You move on to the next product while the last one is still running. Some you keep supporting. Some you shut down or sell, and deal with the fallout: unhappy users, refund requests, bad reviews. And what you built last year can be copied in a weekend.
The things I had done before and was comfortable doing, marketing, consulting, agency work, AI is already taking over most of them. A local service business looked like it would outlast all of them. AI can write your ads and answer your phone. It can’t fix a leaking pipe or repair an AC unit.
My business partner and I tried several things first. None worked. “A service business” isn’t a plan. We needed a specific service, in a specific place.
And we knew almost nothing about the market here. We didn’t even know the terms. Home services, field service, skilled trades. We learned them later.
So we started with what I know best. Data.
Hours to collect, months to analyze
I’ve built scraping tools for years. With Hexomatic, pulling local business data across South Florida took hours: listings, categories, reviews, competitors, locations.
We had one question: which niche isn’t overcrowded but still has real potential?
The obvious ones came first. Plumbing, AC repair, roofing, pest control, pool cleaning. Plenty of demand, and plenty of companies already fighting over it. Getting in meant competing on price from day one.
Going deeper is where it got hard. The data sat in spreadsheets and CSV files that didn’t connect. Every question meant opening several files, filtering, comparing, and starting over for the next one.
Collecting took hours. Analyzing took months.
AI didn’t help much
I used AI heavily during that time. I uploaded files, pasted data, asked the same questions in different ways. It worked for small tasks like cleaning a list or summarizing a trend. For the questions that needed all the data at once, it failed. Answers changed between tries. Numbers didn’t add up. I checked most of it by hand.
For that kind of work, not much has changed since.
What the data showed
COVID changed consumer behavior fast. Some shifts were obvious. Online shopping grew as much in a few months as it had in the previous five years. Food delivery and remote work followed, and large companies moved in quickly.
We were looking for something else: changes that created new local demand that service businesses hadn’t caught yet.
So we ran a second scrape, this time of articles about COVID’s impact on business and consumer spending, and added them to the same dataset as the local business data. That let us compare what was changing with what local companies were offering.
One pattern kept appearing. People spent more time at home and invested in their houses, especially their backyards. Most homeowners said they were more interested in upgrading outdoor spaces than before the pandemic, and outdoor kitchens were near the top of the list. In Florida, where people cook outside most of the year, this was even more visible.
Grill manufacturers confirmed it. Traeger and Weber reported record growth, and both went public. Americans bought over 21 million grills and smokers in about two years.
All that equipment was bought in a short window, so it would need cleaning, maintenance, and repair at around the same time. In our local data, very few companies specialized in it.
The niche had growing demand and limited competition. We chose grill cleaning and maintenance in Miami.
My partner and I launched Grillyan in the summer of 2024. The first order came on August 27, my birthday. The first job took the two of us six hours. Today one technician does it in about two and a half.
Grillyan now has five technicians, more than a thousand clients, and is expanding into other counties.
The tool I built for myself
The research taught me one thing. Collecting data is easy now. Making sense of it is the hard part.
So while Grillyan was starting, I built a system for myself. A local database on my computer with everything in one place: Grillyan’s clients and jobs, Hexact’s customers and payments, competitor data, my articles, meeting notes. I connected it to Claude so I could ask questions in plain English and get answers from real records.
Questions that took an afternoon started taking a minute. Claude stopped guessing because it was reading my actual numbers.
For a long time it was only mine.
The moment it clicked
Then I finished my book, Bones: 21 Clichés Running and Ruining Your Business. 211 pages that needed a serious proofread. English isn’t my native language, and over that many pages, a lot of issues pile up.
I gave it to Claude. It did solid work on the first 20 or 30 pages. Then it lost track of tone decisions from earlier chapters, hit its output limit, and every “continue” made it worse. Maybe 15% of the book got a proper review.
So I imported the book into my database instead. Each page became one row of clean text. Now I could run checks across the whole book, like “list every sentence longer than 35 words,” and the quality stayed the same from page 1 to page 211.
That’s when I saw it wasn’t my problem only. Anyone with a long document, a big contact list, or years of business data hits the same wall. AI has the skills. It can’t handle the input in the shape most people give it.
When I wrote about my setup, people replied asking if they could pay me to set it up for them. That became Second Brain.
Big companies, same problem
Right now we’re in active talks with a few very large companies that want to implement this inside their operations. Nothing is signed yet, but the interest is serious.
These companies have invested heavily in technology and AI over the last few years. They still haven’t solved a basic problem: asking a question across a huge amount of their own data and knowing the answer is based on all of it. Not most of it. All of it.
Current AI models can take in a book’s worth of text in one prompt. That doesn’t solve it. The more text you give a model, the more it skims. Some records get read, some get skipped, and you can’t see which.
In coding, you can live with that. Ask AI to review code, it finds some issues. Run it again, it finds new ones it should have caught the first time. Run it a third time, more gaps. You keep iterating until it works.
With numbers and legal documents, you can’t work that way. If a model reviews a thousand contracts and misses forty, or totals payments and skips a few hundred rows, the answer isn’t slightly off. It’s wrong, but it looks right.
Second Brain changes what the AI reads. Claude doesn’t skim raw text. It queries organized records, and a query returns every row that matches. Nothing gets skipped, and nothing depends on which run you’re on.
What’s new this week
The latest Second Brain update adds more file formats and bigger file support, faster imports, bug fixes, and UI improvements.
Download it at brain.hexact.io. New to Second Brain? Start the 7-day free trial, no credit card required.
Want to see it on your own data? Book a demo.
No time to collect and import it yourself? Our Done For You service gathers your data and loads it into Second Brain for you.
