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CapabilitiesNine practices under one delivery structure. Most engagements draw on three or four of them at once — which is the point, because handing a system between separate vendors is where most programmes lose their schedule.
01
New products and platforms built so your own team can maintain them after handover. That means typed interfaces end to end, tests where they earn their keep, architecture decision records, and no framework only we understand.
02
We treat a model as one component in a system that has to be observable, bounded and reversible. Every AI feature we put into production ships with an evaluation suite of real cases, a defined failure behaviour, a cost ceiling and an audit trail.
03
Everything defined in code, nothing configured by hand in a console at two in the morning. Reproducible environments, deployments that reverse in one command, and cost engineering treated as an ongoing discipline rather than an annual panic.
04
Replacing a system the business depends on, without a cutover weekend. We put a seam around the old application, move capabilities across one at a time, and run both in parallel until the numbers agree — so a rollback is always available.
05
Applications that behave correctly on a bad connection, because that is where they are actually used. Offline-first data, conflict resolution that does not lose work, and release automation that pushes to both stores from a single tag.
06
Ingestion, modelling and a semantic layer with one agreed definition per metric. The test of success is boring and specific: two people in different departments ask the same question and get the same number.
07
Quality is a gate in the pipeline, not a phase at the end. Regression suites that run on every merge, load tests against realistic traffic shapes, and security testing early enough that the findings are still cheap to fix.
08
Running the system, not just delivering it — against a written service level with response and resolution targets. We take on applications we built and applications you inherited, including ones nobody documented.
09
Named senior engineers embedded in your organisation, reporting into your leads, for capacity you need for two quarters rather than two years. Your board, your ceremonies, your definition of done.
An AI engagement with us is four stages long and each one can end it. That is deliberate: the cheapest AI project is the one you cancel in week three with evidence, not the one that reaches production and cannot be measured.
01
Before any model, we write down what the system must decide, how often a human does it today, and how long that takes. That number is the baseline every later claim is measured against — without it, "the AI works" is an opinion.
02
Most disappointing AI projects are retrieval projects that were never done. We index your real corpus, fix the chunking and the permissions, and prove the right passage comes back before a model is asked to reason over it.
03
A pilot ships with a graded test set drawn from your own history, a scoring rubric your team agrees with, and results you can read. If it does not beat the baseline, we say so and you have lost weeks, not a year.
04
Cost ceiling, drift alarm, prompt-injection boundary, fallback path and an audit log of every decision the model made. Somebody is on call for it, the same as for any other production service.
When an enquiry ends here it still ends in something useful: a short written note explaining what to build instead.
