AI Solutions

Most organisations have been shown what language models can do in a demo and are unsure what they should do in production. Pilots multiply, few reach users, and the ones that do are hard to trust: answers vary, costs are unpredictable, and nobody can say what data left the building. The technology is capable; the missing part is engineering.

Support and service desks handling repetitive questions, teams that spend hours summarising or classifying documents, processes that need information extracted from forms and emails, and knowledge that exists in files nobody can search. It also appears as staff pasting company data into public tools because nothing internal exists.

We start with the problem and decide honestly whether a language model is the right tool; sometimes a rule or a search index is better. When it is, we integrate commercial or open models through their APIs, ground answers in your own data through retrieval, and wrap the whole in evaluation, guardrails and monitoring. Data boundaries are explicit: what is sent to which provider, what is stored, and for how long.

A short assessment identifies one or two use cases with clear value and measurable success criteria. We build a first version against real data in a few weeks, evaluate it against a test set your team helps define, and only then widen the rollout. Costs and latency are measured from the first week so there are no surprises when usage grows.

A working feature, and the evidence that it works.

• Use case assessment with success criteria, data boundaries and cost estimate
• Integrated AI feature with retrieval, guardrails and monitoring
• Evaluation set and results, and an operations guide for your team

Hours returned to your people on the work that was consuming them, answers that are grounded in your own information, and an AI capability you can explain to a client or a regulator: what it does, what it sees, and how well it performs.