A demo is easy. A language model doing real work inside a business system is a different problem, because the output has to be correct, checkable and cheap enough to run at volume.

We build the parts that make that possible: retrieval over your own data so answers are grounded rather than invented, structured output that downstream systems can rely on, evaluation so a prompt change cannot quietly break production, and cost control so the bill does not surprise you in month three.

We are also willing to tell you when a language model is the wrong tool. A good deal of what gets proposed as AI work is better served by a database query.

What this covers

Retrieval over your own documents, catalogue or records
Structured extraction from PDFs, invoices and scanned documents
Content generation with a human review step where the stakes justify it
Classification, routing and summarisation inside existing workflows
Evaluation suites, so a prompt change cannot silently degrade output
Cost and latency control — caching, token budgets, model routing
Guardrails and fallbacks for when the model gets it wrong

How engagements work

Feasibility

Whether a model is the right tool, said honestly before you spend anything.
Prototype

A narrow version against your real data, with accuracy measured.
Build

Production integration with evaluation, guardrails and cost controls.
Monitor

Accuracy and spend tracked after launch, because both drift.