Validated before built, evaluated before trusted
AI projects often fail in the same place: a large build commissioned around a use case nobody has tested against real cases. We sequence the work so the expensive decisions wait for evidence. A short discovery fixes the task and the success criteria; a prototype on your data is measured against an evaluation set and put in front of the people who will use it; the production build is commissioned on those measured results — or not at all. Not every AI idea should be built, and part of this service is telling you when a simpler solution serves you better.
What we build
- Assistants over your own knowledge. Answers from your documents, wiki, tickets and systems, with an access model that respects who may see what, and sources shown.
- Document processing. Extraction, classification and summarisation of contracts, invoices, forms, e-mails and tickets, delivered into the systems you already use.
- Workflow automation with human oversight. Multi-step processes where the model proposes or initiates a bounded action, with human approval where the consequences require it and a safe failure path when it cannot proceed.
- AI capability in existing products. Search that understands meaning, drafting and review help, recommendations — added to your application or SaaS product without destabilising it.
- Internal copilots. For support, sales, operations or engineering teams, tuned to your terminology and process, with the same access and logging discipline as anything customer-facing.
How the engagement runs
Validate
One or two working sessions with the people who own the task. Together we fix what the system must do, for whom, on which data, and how we will know it works — a set of real cases with expected outcomes. Use cases the available data cannot support are ruled out here, before they cost money.
Prototype and evaluate
A working prototype on your data: the model integration, retrieval over your documents or systems where needed, and a minimal interface. Selected users try it while quality is measured against the evaluation set. You see what it gets right, what it gets wrong and what it costs to run — and decide on that basis.
Engineer for production
The production build adds what a prototype lacks: a maintainable architecture, integrations with your systems, guardrails and human-approval points, output validation, logging and monitoring, cost and consumption limits, and fallback, rollback and safe-shutdown paths. Model provider and hosting are chosen for your data and documented — commercial APIs whose applicable terms and configuration have been verified for the engagement, EU-hosted options, or open-weight models on infrastructure you control. Before rollout, the system goes through a security review against the current OWASP Gen AI Security Project guidance recorded in the engagement — at the time of writing, the OWASP GenAI LLM Top 10 (2026 edition), with the project’s agentic-security guidance where the system takes actions.
Operate and improve
AI systems drift: models change, data changes, users find new ways to use them. The operational plan defines what is monitored — quality, cost, abuse signals — and the change control that applies: which model, prompt and provider changes trigger re-evaluation, and which release gates they must pass. The frequency and the gates are defined in the engagement, so operation is a documented process rather than a promise.
Built by engineers who also assess AI systems
Our AI-security practice assesses generative-AI applications for prompt injection, sensitive-information disclosure, unsafe output handling, excessive agency and insecure tool use — the assessment service is described at AI security. That experience shapes what we build: bounded tool access, guarded retrieval, output validation and investigable logging are part of the design, not a late addition. Where the scope includes it, the finished system is reviewed by engineers separate from the team that built it.