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The sequence

Every phase ends in something you hold, not something we promise. No surprises, no vanity metrics — just evidence of progress or a clear reason to stop.

Most AI programs fail in the handoff between thinking and doing. We run four rows, each with a defined input, output, and decision gate.

  1. Row 01

    Assess

    Weeks 1–3

    We map your operation, data, and constraints on the ground.

    Before we write code we build a shared picture. We interview business and technical stakeholders, inspect your data quality and access patterns, document regulatory and residency constraints, and score candidate use cases by feasibility, risk, and return. The roadmap is sequenced so the first pilot funds the next phase.

    You hold: a readiness report and a costed roadmap.

    • Stakeholder interviews
    • Data estate audit
    • Use-case scoring
    • Vendor/model fit
  2. Row 02

    Pilot

    Weeks 4–9

    One high-value use case, built and measured against agreed criteria.

    The pilot is a controlled experiment, not a prototype demo. We define acceptance criteria up front — accuracy, latency, cost, user satisfaction — then build the smallest system that can meet them in your environment. Every week ends with a scorecard: green, yellow, or red, with evidence.

    You hold: a working system and an honest scorecard.

    • Prototype build
    • Evaluation framework
    • User acceptance testing
    • Security review
  3. Row 03

    Scale

    Quarter 2

    What the pilot proved goes to production, your teams trained on it.

    Scaling means hardening, not duplication. We integrate the pilot into your CI/CD, write runbooks and escalation playbooks, train the operators and end users, and run a governance sign-off. The goal is a system your own teams can run and extend.

    You hold: production systems, runbooks, trained staff.

    • CI/CD integration
    • Runbook & SOPs
    • Team training
    • Governance sign-off
  4. Row 04

    Operate

    Ongoing

    We run, monitor, and improve the estate under a managed retainer.

    AI in production is a living service. We watch latency, error rates, cost per interaction, and model drift; refresh retrieval indexes; re-run regression tests; and report monthly on value delivered and risks mitigated. Continuous improvement is contractually baked in.

    You hold: monthly operations and value reporting.

    • Latency/cost monitoring
    • Model drift checks
    • Monthly value report
    • Quarterly roadmap review