We start with your business, not the technology.
Most AI projects begin with a model and go looking for a problem. We go the other way. We start with how your experts actually work, then build the system around it.
The hard part was never the model.
Frontier models are a commodity. Anyone can call one. What separates a system that works in an expert domain from a demo that impresses for a week is everything around the model: how well it understands your business, how reliably it behaves, and whether your people can trust its output when the stakes are real. That's the work we do. Here's how.
Four steps, from your judgment to working software.
01Immerse
We embed with the people who do the work, to understand the judgment behind it.
Workshops, working sessions, and recorded deep-dives with your experts and workflow owners. We ask the probing questions: not just what you do, but how you decide, where the exceptions live, what a good call looks like versus a defensible one. The output of this phase isn't a requirements document. It's a genuine understanding of how your business reasons.
We don't treat your business as a technology problem. We treat it as a body of expert judgment worth understanding before we build anything.
02Model
We turn that understanding into a formal model of your business that software can run on.
Using an ontology-based approach, we map your entities, your rules, your workflows, and your edge cases into a structured domain model. Then we iterate on it with you until it's right, until you look at the map and recognize your own business in it. That verified model becomes the foundation everything else is built on.
A consultancy maps your business to a slide deck. We map it to a model that software can execute. This is the step almost no one else takes, and it's why what we build reasons from your business instead of guessing at it.
03Harness
We build the control layer that makes AI reliable enough for work where being wrong is expensive.
Models do the probabilistic work. The harness constrains them. It's a deterministic control layer, grounded in your domain model, that governs what the system can do, checks its work against your rules, and makes its output traceable and repeatable. This is how we build agents that automate and augment real workflows rather than demo well and fail in production.
An agent that's right most of the time is worthless in audit, legal, or biopharma. We build agents inside a deterministic harness, so the system behaves predictably and every output can be traced back to your rules. The value was never the model. It's the structure around it.
04Build
Our engineers build the actual application, lightweight, custom, and running on your infrastructure.
We prototype first. You see working software in weeks, not a roadmap in a slide deck. Then we iterate with your team, refine against real use, and move to production. The models underneath can be open source or frontier, whatever fits the problem. What stays constant is that the system is built for exactly how you work, and when it's done, it's yours.
First working prototype in four to six weeks. Production in two to four months. Built on your infrastructure, handed to you to own and run.
Why an ontology, and not just a smarter prompt.
There are three ways to point AI at your business. Only one of them actually understands it.
Professional judgment is knowledge, plus expertise, plus experience. Documents carry the first two. Experience lives somewhere else: in the exceptions your people have learned to handle and the calls they make when the rules run out.
Generic AI
Approximates your business from patterns learned everywhere else.
It doesn't know your rules, your exceptions, or your standards. Confident and often wrong.
RAG
Retrieves your documents and feeds them to the model.
Better grounded, but it still doesn't understand how your business works. It finds text; it doesn't reason from your logic.
Ontology-based (ours)
Reasons from a structured model of your entities, rules, and workflows.
Handles your exceptions correctly, stays aligned as things change, and produces output your experts recognize as right.
We capture your team's experience as part of that model. Each exception becomes a node in a context graph, linked to the rule it bends, the case that raised it, and the expert who made the call. The system handles the next one the way your experts would, and can show why.
RAG retrieves. An ontology reasons. In expert domains, that difference is everything.
You own it. It runs on your infrastructure. There's no meter.
We don't sell subscriptions and we don't hold your system hostage. What we build runs on your infrastructure, with your models and your data, and we hand it to you to own and operate. No per-seat fees, no vendor lock-in, no dependency on us to keep the lights on. First working application in four to six weeks. Production in two to four months. After that, it's yours.
- 4–6 weeksFirst working prototype
- 2–4 monthsProduction version
We'd rather build you something you own than rent you something you don't.
The problems worth building for.
High-stakes work piling up faster than experts can handle it, and generic AI that isn't good enough to help with it.
- 01
Too much expert work, too few experts.
The volume of contracts, filings, reviews, or cases exceeds what your specialists can handle, and generic automation gets the judgment calls wrong.
- 02
Decisions that need to be fast and defensible.
You need answers quickly, but in your field a fast answer that can't be traced or defended is a liability.
- 03
Institutional knowledge that walks out the door.
Your best reasoning lives in senior people's heads, and every departure costs you expertise you can't easily replace.
Let's map your hardest workflow.
The best way to understand how we work is to point it at a real problem. Tell us the workflow that's eating your experts' time, and we'll show you what our approach would do with it.