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AI features and workflows that actually ship to production.
Overview
We help teams move past the AI demo stage and into real production use. That means retrieval pipelines, evals, guardrails, cost controls and latency budgets — the unglamorous engineering work that turns a chatbot prototype into a feature customers can actually depend on.
What we do
An honest assessment of where large language models genuinely help — and where they quietly don't.
Chunking, embeddings, vector storage, retrieval, reranking and citation, built properly.
Function-calling agents with real guardrails, observability, and human review where it actually matters.
Golden-set evals, prompt versioning, cost dashboards and latency budgets.
Tech we use
How we work
Use-case interviews combined with feasibility scoring.
A working spike within 2-3 weeks, measured against a real eval set.
Auth, rate-limits, caching, fallbacks and observability, all built in.
Continuous evals alongside cost and latency monitoring.
Prompt, model and retrieval improvements, with regression safety built in.
What you get
FAQ
It depends on your cost, latency and capability needs. We evaluate against your own eval set — Claude, GPT, Llama and smaller specialised models all stay in scope.
Constrained retrieval, output schemas, evals and human review on high-stakes flows. There's no silver bullet here — just disciplined engineering.
Send us a quick one-line brief and we'll come back with next steps within 24 hours.
