AI-Powered Applications & Tools
Most AI features are demos that survived into production. We build the other kind — LLM integrations, retrieval systems, and automation pipelines that do measurable work: contracts reviewed, tickets triaged, search that actually finds the thing. If we can't evaluate it, we don't ship it.
The demo-to-production gap
An AI feature is easy to demo and hard to trust. The demo works because someone picked the input. Production is every input — the malformed one, the adversarial one, the one nobody imagined. Plenty of teams ship the demo, discover the gap in front of customers, and quietly walk it back.
We close that gap before launch. Every AI system we build ships with a way to measure whether it's actually working, because "it seemed to work when we tried it" is not a standard you can run a business on.
What we build
LLM integrations
AI woven into your product where it earns its place, not bolted to the side as a novelty.
Retrieval and semantic search (RAG)
Systems that answer from your data, with citations, instead of guessing.
Automation pipelines
Real workflows automated end to end: intake, classification, routing, action.
AI agents
Tools that take multi-step actions under real guardrails, with a human in the loop where it matters.
Internal AI tools
Assistants and copilots that make your own team faster at the work they already do.
Measurable, or it doesn't ship
The difference between an AI feature that helps and one that erodes trust is evaluation. Before anything reaches your users, we define what "working" means for your use case and build the harness to check it — accuracy, cost per run, latency, failure modes. If it can't clear the bar, it doesn't go out. That discipline is the whole difference between AI that does work and AI that makes work.
Built to belong
The best AI in a product doesn't announce itself. It's the search that finally returns the right result, the form that fills itself, the ticket that's already routed before anyone looks. We build AI that disappears into a better experience — not a chatbot in the corner that everyone learns to ignore.
Who this is for
Teams with a real workflow worth automating, products where AI could be a genuine feature rather than a press release, and companies sitting on data that should be answering questions and isn't.
Frequently asked
How long does an AI integration take?
A focused integration — one workflow, one model — typically runs 2–6 weeks including evaluation. Multi-agent or broader automation work takes longer; we scope it after seeing the actual workflow.
Which models do you use?
Whatever fits the problem and your constraints on cost, latency, and privacy. The model is a choice we make per use case, not a fixed allegiance.
How do you stop it from making things up?
Retrieval grounding, evaluation harnesses, and guardrails — plus the honesty to keep a human in the loop where the cost of a wrong answer is high.
Can you add AI to our existing product?
Yes. Much of our AI work is integration into software that already exists and already has users.