Artificial Intelligence
AI development services: evaluated, observable, in production
A convincing prototype takes an afternoon. What decides whether it survives a thousand real users is the evaluation, guardrails, observability and cost control around it. That is what we build.
- EvaluatedMeasured, not assumed
- EUData residency
- SeniorEngineers only
What we build
AI that earns its place in your product
Not a chatbot bolted onto a homepage. Features that remove real work from real workflows.
LLM product features
Drafting, summarising, extraction, classification and search over your own data, with retrieval that cites its sources.
AI agents and workflow automation
Multi-step processes that call your systems and hand off to a human at the points where that matters.
Document and data pipelines
Turn contracts, invoices, tickets and PDFs into structured records your business systems can act on.
Evaluation harnesses
A regression suite for model behaviour, so a prompt change cannot silently degrade yesterday's quality.
Guardrails and fallbacks
Output validation, refusal handling and graceful degradation when a provider is slow or down.
Cost and latency control
Caching, routing between model tiers and hard spend ceilings, so unit economics stay predictable.
How an AI engagement runs
Prove the value before you build the platform
Use-case triage
We rank your candidate use cases by value and by how tolerant each one is of being wrong. The best first project is rarely the flashiest.
Offline evaluation
Build a labelled test set from your real data and measure whether the approach clears your quality bar at all.
Production build
The feature, plus the retrieval, guardrails, telemetry and spend controls it needs to be operated rather than constantly supervised.
Measure and tune
Track quality, latency and cost per action against the baseline, and keep tuning where the numbers say it pays.
Why teams call us second
The prototype-to-production gap is an engineering problem, not a prompting problem
Many of the AI projects we inherit are technically working and commercially stuck. The model does roughly the right thing, and nobody can say how often it is wrong, what it costs per customer, or what happens when the provider changes a model version.
That gap is filled with unglamorous engineering: test sets, versioned prompts, retrieval you can debug, tracing on every call, budget alarms, and a rollback path.
It is the same discipline that makes any other production system trustworthy. We apply it to AI, and it is usually what turns a promising pilot into something a board is willing to fund.
Common questions
What buyers ask about AI projects
Will our data be used to train someone else's model?
Not on our architectures. We use enterprise API tiers with training disabled, keep your data in EU regions, and where the sensitivity requires it we run open-weight models in infrastructure you control.
How do you stop it hallucinating?
It cannot be reduced to zero, so the engineering answer is containment rather than elimination: ground answers in retrieved sources and cite them, validate outputs against a schema before they reach your systems, and route low-confidence cases to a human reviewer.
The residual error rate is then measured against a real test set, so the remaining risk is a figure you have reviewed and accepted.
Which model do you use?
Whichever clears your quality bar at an acceptable cost and latency, and we build so that answer can change. Providers advance quickly, so we design the integration boundary to keep that choice reversible.
Can you work with the prototype we already have?
Yes, and that is a common starting point. We audit what exists, tell you honestly what is reusable, and keep the parts that earn their place instead of restarting for the sake of it.
What if AI turns out to be the wrong tool for our problem?
We will tell you during triage, before you spend a sprint on it. A rules engine or a better database query beats a model more often than is generally acknowledged, and establishing that early is far cheaper for you than discovering it in month four.
Bring us your AI use case
Thirty minutes with a senior engineer, and an honest answer about whether it is worth building.