A consulting team that both assesses AI feasibility and builds it.
One team handles both proposal and delivery, so whoever makes the call also carries it out.
The engineer who ran the assessment carries on into the PoC, so what looked feasible on paper holds up in the build.
To keep this structure we cap the number of concurrent projects.
We started with data analysis work; the first year was mostly assessments.
We stopped handing assessments off. The current structure, where the same people build what they assessed, took shape here.
As regulated data work grew, we standardised how we operate inside a client's own network.
We began verifying the AI capability of acquisition targets against their code and data pipelines.
We moved to an office with a training room, so staff courses can run on the client's own data.
We recommend adoption when results can be measured. Constraints and prerequisites go into the report as they are.
Client data is processed inside the client environment. For regulated data the model sits on-premise. A security undertaking is signed with the contract.
All source code and documents are handed over at the end of the contract, in a form your team can run.
Client names and logos appear only with prior consent. Cases are described by sector and scope.
