AI studio
Assistants and agents can produce a working demo in days. Turning that demo into a system that survives real users, real data and an audit is a different discipline — that is the part we do.
Where you are now
The work depends on how far the idea has already travelled. We scope against the stage you are actually at, not the one on the roadmap.
4–8 weeks
You have a use case and no code. We validate feasibility on your real data, then build a first version with the boring parts — auth, storage, evaluation — done properly from the start.
Validate an idea6–12 weeks
You have something generated fast that works in a demo. We harden it: real error handling, tests, observability, cost control and a deployment path that does not depend on one person's laptop.
Harden a prototype3–9 months
You have users and the architecture is starting to bite. We restructure it into something multi-tenant, measurable and safe to change, without stopping delivery.
Scale a live productWant an outside read before you commit? A production readiness review covers architecture, evaluation coverage, cost per task, failure modes and security posture — delivered as written findings in about two weeks.
Request a readiness reviewWhy this needs doing
These are the failure patterns we see repeatedly when a fast prototype meets production. None of them are reasons to avoid AI-assisted development — they are the checklist for finishing the job.
Generated code compiles and reads well while quietly mishandling an edge case. Without tests written against real scenarios, nobody finds out until a customer does.
Model or prompt changes ship on impression rather than measurement, so quality drifts and nobody can say when or why.
Token spend scales with usage in ways nobody modelled. Cost per task is discovered on the invoice instead of in design.
Business rules end up embedded in prompt strings rather than in code that can be tested, versioned and reasoned about.
Timeouts, rate limits, partial responses and provider outages are unhandled, so a degraded dependency becomes a user-visible outage.
Secrets in code, over-broad tool permissions, and customer data flowing to third-party endpoints that were never reviewed.
Capabilities
Four capability areas, each shipped with the evaluation and operational work that makes it dependable.
AI & automation serviceGrounded answers over your own documents, tickets and data — with chunking, indexing and freshness treated as engineering problems, not defaults.
Agents with scoped permissions, structured outputs and explicit human approval wherever the consequence is financial, legal or irreversible.
Extraction, classification and routing for high-volume operational work, with exceptions escalated to a person instead of guessed at.
Evaluation sets built from your historical cases, schema validation on every output, and dashboards for accuracy, latency and cost per task.
How we work in AI
We keep what works in your prototype and replace only what cannot go to production. A rewrite is the last option, not the opening move.
Nothing ships without a labelled evaluation set drawn from real cases and an agreed quality bar.
Draft-first workflows: the system prepares, a person approves anything with money or legal weight attached.
Provider-specific code stays behind an interface, so a better or cheaper model is a configuration change.
Cost per task is estimated during design and monitored in production, with caching and routing used before bigger models are.
Prompts, evaluations and pipelines live in your repository with documentation, so your engineers can operate them without us.
Case studies
Problem, constraint, decision, consequence. No vanity metrics, no logos we are not allowed to name.
All case studiesSample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.
Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.
A nightly batch pipeline that had outgrown its window, re-architected into an incremental streaming system with correctness checks that alert before customers notice.
An AI agent that triages production alerts, reconstructs root cause and opens a reviewed pull request — removing the overnight on-call rota without removing the human approval step.
AI studio FAQ
The part of our team that specialises in getting AI features into production: retrieval systems, agents, workflow automation, and the evaluation and cost work that keeps them dependable.
Yes — that is a common starting point. We assess what is safe to keep, harden the rest and add tests, observability and deployment automation around it.
We look for high-volume, judgement-light, text- or data-heavy work with a tolerant error budget and an existing review step. If the task fails those tests, deterministic software is usually cheaper and safer, and we will say so.
A feasibility assessment is two to three weeks. Getting a prototype to production is usually six to twelve. Platform-scale work runs in quarters, re-planned every increment.
Evaluation harnesses keep running, cost and quality stay monitored, and your team takes over with documentation and runbooks whenever you choose.
Start a conversation
Tell us what you are trying to build, fix or decide. If we are not the right team for it, we will say so and point you somewhere better.