AI engineering
AI features that make it to production
Getting an LLM to do something impressive takes an afternoon. Getting it to do the same thing reliably, for every user, at a predictable cost — that is engineering, and that is what we do.
30 minutes with an engineer. No commitment.
What we build
Every item ships with evals, monitoring, and a human path
The discipline
AI engineering is a production discipline
The gap between an AI demo and an AI feature is where most projects die: edge cases, hallucinated answers in front of clients, and a cost per query nobody modeled. We close that gap with the same rigor as any other engineering work.
[ Evals ]
Eval sets from your real cases
Accuracy is a number you see, not a feeling after a demo.
[ Guardrails ]
Graceful fallbacks
Output validation and a safe path when the model is wrong.
[ Observability ]
Tracing and cost per query
Every answer is traceable and every query has a price tag.
[ Humans ]
Approval where stakes are high
Human-in-the-loop exactly where a wrong answer is expensive.
The stack we work with: Claude / Anthropic ecosystem · OpenAI and open models · AI agents · MCP · RAG and vector search · evals, tracing, observability · EU-hosted or self-hosted · Python, FastAPI, TypeScript · PostgreSQL and vector stores.
How we work
From use case to running feature
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[ 01 ]
Use-case audit
We rank your candidate use cases by value, risk, and feasibility — against your real data. Some ideas die here, cheaply.
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[ 02 ]
Prototype with evals
A working prototype measured on an eval set from day one. You see the accuracy number, not just a demo that went well.
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[ 03 ]
Production hardening
Guardrails, fallbacks, cost controls, monitoring, integration with your product. The distance most AI projects never cover.
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[ 04 ]
Monitor and improve
Accuracy and cost tracked weekly. Models change fast; the feature keeps up without a rebuild.
Related
Related services
[ Processing per invoice ]
minutes of manual workseconds, automated
[ Manual data entry ]
the default for every documentexception — flagged edge cases only
FAQ
Common questions about ai engineering
Our data is sensitive. Where does it go?
Where you decide: EU-hosted APIs, private cloud deployments, or open models on your own infrastructure. No training on your data, and the data flow is documented before anything runs.
Which model will you use?
The one that wins on your eval set at an acceptable cost. We benchmark on your real cases and often mix models — a strong one for hard steps, a cheap one for volume.
How do you handle hallucinations?
Grounding in your data with citations, eval sets that catch regressions, and human approval where the cost of a wrong answer is high. The risk level is a per-use-case decision you make with numbers in front of you.
Should we build or buy?
The use-case audit answers that honestly. If an off-the-shelf tool covers your case, building custom is waste — and we will tell you so.
How long until something runs in production?
Narrow-scope features typically go from kickoff to production in weeks. We rank your candidate use cases by value, risk, and feasibility first, so the first thing we build is the one with the fastest payback.
Start with the use case, not the model
Bring your candidate use cases. The audit tells you which one pays back fastest — and which ones to drop.