Where AI pays off first
The best first projects are frequent, slow and well understood: reading documents, drafting replies, classifying requests or preparing comparisons. They have clear inputs, a known good result and a person who can review the output.
We start there because the value is easy to measure, and because the controls you build for the first workflow are reused for the next ones.
Choosing models without lock-in
No single model is best at everything, and prices and quality change often. We design a routing layer so each task can use the model that fits its quality, speed and cost needs, and so a provider can be replaced without rebuilding the workflow.
Where data must stay inside your organisation, orchestration and, where feasible, the models themselves can run on infrastructure you control.
Keeping people in control
Every AI workflow gets explicit limits: what it may read, what it may change, and which actions need approval. Above those limits the system prepares the work and a person decides.
All runs are logged with inputs, outputs, approvals and cost, so you can review what happened and improve the process.
Measuring quality and cost
We build a test set from your own cases before launch and score every change against it. In production we track quality, cost and latency per workflow, so decisions about models and prompts are based on evidence rather than impressions.
