AI that survives contact with your operations
Most AI initiatives die between the demo and the P&L. We build the unglamorous 90% — data pipelines, evaluation, guardrails, workflow integration — so the model actually runs your process, with a business metric attached to prove it.
Where we deploy it
Document intelligence
Contracts, claims, invoices and compliance filings — extracted, classified and reviewed at machine speed with accuracy thresholds you set.
Copilots for teams
Sales, support and operations assistants grounded in your data — with citations, permissions and audit trails built in.
Agentic workflows
Multi-step back-office processes — triage, reconciliation, order handling — executed end-to-end with human checkpoints where they matter.
Forecasting & decisions
Demand, pricing and risk models embedded in the tools where the decision is made — not in a dashboard nobody opens.
Why our deployments stick
Four disciplines that separate production AI from pilot theatre.
Metric first
Every engagement names the number it must move before a line of code is written.
Evaluation harness
A test set from your real cases, scored continuously. If accuracy drifts, we know before you do.
Human checkpoints
Confidence-based routing: the model handles the routine, people handle the exceptions.
Model agnostic
Frontier API, open weights or fine-tuned — chosen per task on cost, latency and privacy.
From idea to production, in three steps
AI engagements carry more uncertainty than classic software — so we structure them to spend the least money proving the biggest risk first.
Feasibility sprint
We test the riskiest assumption against your real data: can a model hit the accuracy your process needs? You get a written verdict with measured numbers — go or no-go.
Production build
Pipelines, guardrails, evaluation harness, workflow integration, permissions and audit trails — the unglamorous 90% that makes the model usable by your team daily.
Measure & improve
Continuous evaluation against your live cases, drift alerts, and monthly reviews against the business metric the deployment answers to.
Data, privacy & governance
The questions your legal and security teams will ask — answered before they ask them.
Common questions
Do we need our data "ready" first?
No. Messy data is normal — the feasibility sprint works with what you have and tells you exactly what's missing, if anything.
What accuracy can we expect?
We don't promise a number before measuring — that's what the feasibility sprint is for. You get measured accuracy on your own cases before committing to the build.
Which models do you use?
Whichever wins on your task, cost, latency and privacy constraints — frontier APIs, open weights or fine-tuned models. The architecture keeps the model swappable.
What happens when models improve?
Your evaluation harness makes upgrades safe: we re-run the test set against new models and switch only when the numbers say so.
A law firm first-passes every contract through its own review model
Lawyers review flagged clauses instead of reading page one to signature — delivered on the signed estimate.
Read the case study →Bring us the workflow. We'll tell you if AI belongs in it.
Honest scoping — including "a rules engine would do this cheaper."