®AI product engineering
Model-backed features designed as product: streaming interfaces, sane failure states, and a fallback path for every call that can time out.
Solution — AI
From retrieval to agents — AI that ships behind a real product, not a demo.
Most AI work fails in the same place: it demos well and behaves badly in production. We build the unglamorous half — retrieval that returns the right document, evaluation that catches regressions before users do, guardrails and fallbacks for the day the model is slow or wrong — and wire it into software people already use.
(01) — What we do
RAG & knowledge assistants
Assistants grounded in your own documents — chunking, embeddings, reranking and citations, tuned until the retrieved context is actually the right one.
Agentic automation
Tool-using workflows that take real actions in your systems, with scoped permissions, audit trails and a human checkpoint where the stakes justify one.
Computer vision
Detection, classification and inspection pipelines for imagery and video, including the labelling and data work that decides whether they hold up.
Model integration & evals
Provider-agnostic integration behind one interface, with an evaluation suite that scores changes so a prompt edit stops being a leap of faith.
AI search visibility
Entity and schema work that makes a brand legible to AI answer engines, so the summary a model gives about you is drawn from sources you control.
(02) — How it runs
01
Find the honest use case
We look for tasks with a clear input, a checkable output and a cost worth removing. Anything without those three is a research project, and we say so early.
02
Prototype against real data
A thin slice on your actual documents and edge cases inside weeks, scored with an evaluation set rather than a demo script.
03
Harden and ship
Guardrails, rate limits, caching, observability and cost controls, then a staged rollout with the quality metrics visible to your team.
What you hold at the end
- Production AI features inside your product
- Retrieval pipelines and vector indexes
- Evaluation suites and regression tracking
- Prompt and tool definitions under version control
- Cost, latency and quality dashboards
(04) — Straight answers
Do we have to send our data to a third-party model?
No. We design for the deployment you are comfortable with — hosted APIs with zero-retention terms, a private cloud endpoint, or open-weight models running in your own infrastructure.
How do you stop the model from making things up?
Grounding, citation and refusal paths, plus an evaluation suite that measures hallucination rate on your own questions. Where the answer matters, the interface shows its sources and the confidence it has.
Can you work with an existing product and team?
Usually that is the brief. We integrate with your codebase, review conventions and release process, and hand over prompts, evals and documentation so the feature stays maintainable without us.