How we work

Six stages. One accountable plan. No surprises at cutover.

We architect the AI systems that run your business — so revenue compounds while manual work disappears. Every engagement follows the same sequence — measured, architected, built, and proven in parallel before it ever touches production for real.

The engagement

A vertical timeline, not a fixed-price black box.

Each stage has a duration, a promise, and deliverables you can hold us to — plus what we need from your team to move at that pace.

  1. 01

    Diagnostic

    Week 1–2

    We measure before we recommend.

    We shadow the people doing the work, instrument the current process, and put a number on every handoff. You get a baseline you can hold us to.

    Deliverables

    • Process map with hour and cost baselines
    • Opportunity portfolio, scored
    • Data and access audit

    What you do

    Grant access to the systems in scope and make the people doing the work available for a few hours of shadowing.

  2. 02

    Architecture

    Week 2–4

    Decide once, in writing.

    Target-state design, build-versus-buy calls, and the boring decisions — data ownership, failure modes, review gates — documented before code.

    Deliverables

    • Target architecture
    • Architecture decision records
    • Costed delivery plan

    What you do

    Review the target architecture and sign off on the decision records — this is the point to push back.

  3. 03

    Foundation

    Week 4–7

    The unglamorous layer, done properly.

    Integrations, data contracts, observability, and the evaluation harness. Everything after this moves faster because of it.

    Deliverables

    • Integration layer with contract tests
    • Observability and cost monitoring
    • Evaluation harness

    What you do

    Nominate an engineering contact to pair on integrations and validate data contracts.

  4. 04

    Build

    Week 6–12

    Working software every week.

    Weekly demos against real data. Deterministic logic stays explicit; models are used where variance is expected and always evaluated.

    Deliverables

    • Production workflows and surfaces
    • Regression suite in CI
    • Weekly demo record

    What you do

    Show up to weekly demos and react against real data — silence here is the most expensive mistake.

  5. 05

    Shadow & cutover

    Week 10–14

    Prove it in parallel first.

    The new system runs alongside the old one until accuracy and cost clear the agreed thresholds. Then we cut over by segment, never all at once.

    Deliverables

    • Shadow-mode accuracy report
    • Phased cutover plan
    • Rollback procedure

    What you do

    Agree the accuracy and cost thresholds up front, then watch the shadow-mode report with us.

  6. 06

    Ownership

    Week 14+

    You own it, or we run it. Your call.

    Documentation, runbooks, and enablement so your team can extend the system. Optional operating retainer for tuning and on-call.

    Deliverables

    • Runbooks and documentation
    • Team enablement sessions
    • Optional operating retainer

    What you do

    Send the team through enablement sessions and decide whether you're operating it or we are.

Operating principles

The rules we don't bend for a deadline.

Measure first, automate second

If we can't baseline it, we won't claim to have improved it. Every engagement starts with numbers.

Deterministic where it counts

Business rules belong in code that can be tested. Models handle language and variance, not policy.

Autonomy is earned

Systems start supervised and graduate as evaluations prove them. No automation ships on faith.

You own the outcome and the code

Your repository, your cloud, your data. No black boxes, no hostage architecture.

Risk, addressed in writing

What could go wrong, and how we de-risk it.

We would rather name the failure modes up front than discover them together in week ten.

Risk

Scope creep once the team sees what's possible

Costed delivery plan is fixed at the Architecture stage; new ideas go into a scored backlog, not the current sprint.

Risk

The model gets something wrong in production

Confidence thresholds route uncertain cases to a human, every run is logged and replayable, and a regression suite runs in CI.

Risk

Cutover breaks something the old system quietly handled

Shadow mode runs the new system in parallel until accuracy and cost clear agreed thresholds, then we cut over by segment, never all at once.

Risk

Your team can't extend the system after we leave

Documentation, runbooks, and enablement sessions are a deliverable, not an afterthought — with an optional operating retainer if you'd rather we keep tuning it.

Start with diagnostics

See the first stage applied to your process.

A 30-minute call to scope what a diagnostic would look like for your team, with no commitment beyond that.

Typical reply within one business day