About

Our Story

## Before

In early 2023, I was deep in the trenches of a regulated-industry workflow. The incumbents were expensive, sluggish, and built on assumptions from a decade ago. Every tool I evaluated either lacked domain specificity or required a 6-figure implementation budget.

## The Turning Point

When LLMs matured enough to handle structured domain output, I stopped evaluating and started building. First as an internal tool. Then as something I couldn't stop sharing with peers in the same vertical. The pattern was consistent: domain experts wanted AI that understood their language—not AI that had to be taught their language every session.

## After 8 months

The MVP went live in week 3. The first paying customer signed in month 2. By month 6, I had processed over 12,000 domain-specific requests without a single hallucination flag in production. The system wasn't impressive because it used AI. It was impressive because it applied AI precisely where domain knowledge made the difference.

## The insight that never left me

Vertical AI isn't about adding a chatbot wrapper. It's about encoding the decision logic, the terminology, and the edge-case handling that only insiders know. That encoding is what we built. That's what we ship.

How We Operate

Precision over Generality

We refuse to ship a feature that works for 80% of users and fails silently for the rest. Every output is traced, every edge case documented, every error surfaced explicitly.

Domain-First Architecture

LLMs are a component, not the product. Our systems are built around domain ontology first. The model follows the structure—not the other way around.

Auditability by Default

In regulated workflows, 'it works' isn't enough. Every decision, every confidence score, every fallback path is logged and reviewable. Compliance isn't an afterthought.

No Black-Box Surprises

When you ask why the system returned a given output, you get an answer—not a shrug. Explainability isn't a premium tier. It's the baseline architecture.