How we work

Four phases. Every one ends in something you own.

No open-ended discovery. Each phase has a fixed length, a defined output, and a decision point where continuing is your choice.

  1. 01

    Discovery

    1 week

    We sit with the people doing the work, walk one real workflow end to end, and find out where the data lives and who may see it. Most of the risk in an AI project is discovered here — or paid for later.

    Output

    Scope, success criteria and a written integration plan

  2. 02

    Prototype

    2–3 weeks

    A working slice on your real data in a sandbox — not a demo on synthetic examples. In parallel we build the evaluation set that decides, objectively, whether this is good enough to ship.

    Output

    A running prototype plus an evaluation set with a baseline score

  3. 03

    Production

    4–6 weeks

    Integration into the live systems, access handled the way your security team requires, rollout to a pilot group and then to everyone. Monitoring and cost controls go live with the feature, not after it.

    Output

    The integration live for real users, with monitoring and a rollback path

  4. 04

    Operations

    Ongoing

    Models change, your processes change, and quality drifts. We keep the evaluations running in CI, watch spend and latency, and integrate the next workflow — or hand the system over.

    Output

    SLA-backed operation, quarterly reviews and a roadmap for the next workflow