LLM applications & agents
Assistants, copilots and multi-step agents wired into your tools and data, with guardrails from day one.
Engineering
Prototype → Production
We design and build AI systems that hold up in the real world — measured, maintainable and owned by your team.
Most AI projects stall between a promising demo and a dependable product. We close that gap with engineering discipline: clear success metrics, evaluation from the first commit, and systems your team can understand and run.
RAG & knowledge systems
Retrieval over your documents and databases that returns grounded, citable answers — not confident guesses.
Evals & reliability
Test suites, LLM-as-judge pipelines and monitoring so you know when quality moves, before your users do.
Production MLOps
Deployment, observability, cost controls and hand-over so the system keeps running after we leave.
Who it’s for
Built for people who need results, not hype.
- Engineering leads with a promising prototype that isn't production-ready
- Product teams adding AI features to an existing platform
- CTOs who need a senior AI team for a defined build, without a long hiring cycle
How an engagement runs
Clear steps, no surprises.
- 01
Scope
A short technical discovery to pin down the use case, data, constraints and success metrics.
- 02
Prove
A thin end-to-end slice with an eval harness — evidence before investment.
- 03
Build
Iterative delivery in your repo, with weekly demos and measured quality.
- 04
Hand over
Documentation, runbooks and pairing so your team owns it fully.
Common questions
Do you work in our codebase and cloud?
Yes. We build in your repositories and infrastructure so nothing is locked to us.
Which models and vendors do you use?
Whatever fits the problem — commercial APIs, open-weight models or both. We recommend based on quality, cost and data-residency needs.
How long does a typical build take?
A proof slice usually takes 2–4 weeks; a production system 6–12 weeks, depending on scope.
Let’s build what’s next, together.
Drop us a line. No pitch deck — just a conversation about where AI can help.