Six People and the Machines

There are six of us. We run three products - an EHR, an e-signature platform, and an AI scribe - serving thousands of clinicians every day, from solo vets to hospital networks. People assume the explanation is AI, and they’re right, but almost everyone guesses the wrong part of the explanation.

So here is what “AI inside the company” actually looks like for us, concretely - and what it looked like before AI existed, because the philosophy is twenty years older than the tools.

Coding with agents is the bare minimum

Yes, we code with AI agents - multiple agents working in parallel, every day. I want to be clear about how unimpressive that is in 2026: it’s the bare minimum. Table stakes. If “we use Copilot” is your company’s AI story, you don’t have an AI story.

The leverage isn’t in coding faster. It’s in what becomes buildable for a six-person company that was never buildable before.

Example one: the forensics of 0.2%

About 0.2% of our recordings still fail. At thousands of consultations a day, that’s not a rounding error - it’s dozens of real people, daily. And the stakes are not abstract: picture a physician in a difficult, life-changing consultation who takes no notes because he trusts our product to capture everything. If we lose that recording, “only 0.2%” is a meaningless consolation. For him, it was 100%.

So one of my actual jobs as a founder is to stay on top of every single failure - read it, understand it deeply, and personally message the affected person to explain and apologize.

What makes that possible at scale is instrumentation we built ourselves: forensic tooling that reconstructs exactly what happened on that specific device in that specific consultation - which of the forty-plus ways a recording can die actually killed this one. In the early days, one investigation could eat my afternoon. Now AI does the heavy lifting of the analysis, and I do the part that must never be automated: understanding, deciding what we fix, and facing the customer.

That’s the shape of it: AI doesn’t replace the apology. It makes the apology fast, informed, and followed by a fix.

Example two: the CRM we built in a week

We love Intercom - genuinely great software, and for years it earned its $1,000 a month from us. But our customers live on WhatsApp, because that’s where Brazilian clinics actually talk, and no off-the-shelf AI responder met the bar we hold for conversations with doctors.

The old build-vs-buy math was obvious: building a CRM takes months, so you buy the closest fit. That math is dead. I built our own CRM - WhatsApp-native, shaped exactly around how we support and sell - in one week. Not a prototype; the tool the team runs on, with a fit to our workflow no vendor could ship because no vendor knows our workflow like we do. The subscription savings are the footnote. The point is: when building costs a week, every subscription on the books becomes a question.

This philosophy predates AI by a decade

Here’s what most “AI-native company” commentary misses: the leverage was never the technology. It was the habit of automating around human attention - and we were doing it long before LLMs.

Years ago we built automation that watched how each new user actually used the product, segmented by specialty. We knew, for example, that a psychiatrist’s killer feature is controlled-substance prescriptions. So if a psychiatrist signed up, used the product for three days, and hadn’t created a controlled prescription, an email went out - personal in tone, hand-written in style, with an animated GIF showing exactly how easy the feature was. Those emails converted remarkably. The same machinery watched for usage drops and quietly checked in on important accounts.

That’s also why support never needed a department: a product that answers questions before they’re asked, plus automation that flags who needs human attention, let one full-time person (plus a backup) deliver sub-one-minute response times with 98% positive ratings. We could double the customer base without adding anyone.

Replace “hand-built email rules” with “agents and custom tooling” and it’s the same company, with a bigger lever.

The actual formula

People want the formula to be a tool list. It isn’t. The formula is:

Talk to customers - keep founders in the support channel, keep the WhatsApp link on every page.
Notice what eats human attention that a machine could carry.
Build exactly that - and now, with AI, “build” costs a week instead of a quarter.

AI didn’t change our method. It changed the exchange rate between attention and software. Six people was always the right size for this company. The machines just keep making six people bigger.