AI implementation

What we build

Retrieval over your own material

Question answering, search and drafting grounded in your documents, policies, contracts, case files or records, built to survive a security review. Permissions resolved per user at query time. Every answer cited to a source someone can open. A corpus with an owner and a lifecycle, so superseded documents stop producing confident wrong answers.

Document and data extraction

Unstructured material turned into structured fields you can act on: invoices, forms, correspondence, reports, scanned records. Usually the highest-return AI work available: the current process is someone retyping, and accuracy can be measured.

Workflow automation

Classification, routing, triage, summarisation and drafting, wired into the systems where the work happens. A separate tool people must remember to open does not get opened.

Agents, where they fit

Multi-step systems that plan and use tools, for tasks that vary each time. Tight tool permissions, human approval wherever an action has consequences, and full logging of what was decided and why. Our agent setup and security service covers the containment in depth. A deterministic pipeline does the job better more often than the market suggests, and the assessment says when.

How we build

Real data early

We work on a slice of your real material under a proper agreement, as early as your governance allows. Synthetic-data pilots prove a demo can be built and little else. Real corpora are messier, less consistent and more sensitive than any demo sample.

Evaluation as infrastructure

Before we tune anything, there is a test set of real questions with reviewed correct answers, and every change is measured against it. So when the person approving rollout asks "how accurate is it?", you answer with a number.

Boundary first

Data classification decides the architecture, and we settle it in week one. Retrofitting a boundary is a rebuild, and the most expensive mistake in this field.

Built to be handed over

Tests, CI, infrastructure as code, architecture decision records, a runbook, and sessions with your engineers. Clients tell us afterwards this is the part that mattered most.

How an engagement runs

Weeks 1-2: Scope and boundary. Confirm the use case, settle data classification and deployment tier, build the evaluation set, agree what success means and who signs it off.

Weeks 3-8: Build. Working software from the first fortnight, reviewed with you every two weeks against the evaluation set. Integration runs in parallel, as it is usually the long pole.

Weeks 9-12: Harden and hand over. Security review, monitoring, load and failure testing, documentation, handover sessions, and a defined support period while your team takes ownership.

What we will not do

• Build something that should not exist. If a rule engine or fixed query does the job, that is the recommendation, and it costs you less.
• Put our own licensed middleware permanently between you and your systems.
• Ship without an evaluation set. Then nobody, including us, knows whether it works.
• Claim a human-in-the-loop control where the human would be approving faster than they could read.