Most AI projects die as demos. We build the unglamorous parts - retrieval, evals, guardrails and monitoring - so your AI features actually reach users and stay reliable.
Answer questions over your own documents and data, with citations and the retrieval quality to make it trustworthy.
In-product assistants that take real actions through tools - not just chat windows bolted onto a sidebar.
Classification, extraction, scoring and recommendations wired into your workflows and UI.
A weekend prototype is easy. Making it accurate, fast, observable and safe under real load is the work - and it’s what we do.
def answer(q): docs = retriever.search(q, k=8) ctx = rerank(docs)[:4] return llm.generate( prompt=build(ctx, q), guardrails=True, ) # cited + evaluated
Test suites that score quality on real examples, so changes are measured, not guessed.
Input/output checks, PII handling and refusal behaviour appropriate to your domain.
Tracing, cost and quality dashboards so you see drift before your users do.
We’re model-agnostic - OpenAI, Anthropic Claude, and open-weight models. We pick based on your accuracy, latency, privacy and cost needs, and keep the architecture portable.
No. We use enterprise/API tiers with no-training guarantees, and can deploy open models in your own environment when data residency demands it.
With evaluation sets built from your real data. We track accuracy, hallucination rate, latency and cost on every change before it ships.
Tell us the problem. We’ll tell you honestly whether AI is the right tool - and how we’d build it.