Fixed-scope engagements, staged from problem to production.

Stage 1 · 1–2 weeks

AI & Data Opportunity Sprint

Find the workflow worth building first.

Best for: Leaders who know AI should help but have not yet picked a bottleneck, a data source, or a metric.

What you get

• System and data map across your stack
• Three prioritized use cases scored on value, readiness, and risk
• ROI hypothesis and success metric for the top candidate
• Risk and governance review
• Fixed-scope delivery plan for a production pilot

Outcome: A decision you can defend and a plan you can execute.

Stage 2 · 4–6 weeks

Production AI Pilot

One governed workflow, on real data, with a measurable result.

Best for: Teams with a chosen use case who need it running on live systems, not in a sandbox.

What you get

• Integrations to the systems in scope
• Working workflow with human approval steps
• Access controls, logging, and source citations
• Evaluation set and baseline results
• Success-metric instrumentation and a go/no-go readout

Outcome: A production workflow your team uses, with evidence of impact.

Stage 3 · 8–16 weeks

AI System Build

From one workflow to a platform your team can extend.

Best for: Companies ready to move beyond a pilot into a governed, multi-workflow AI system.

What you get

• Production integrations and data layer
• Model gateway with policy-based routing
• Agent workflows with approvals and escalation
• Monitoring, evaluation, and audit logging
• Security controls and documentation for your governance program

Outcome: A sovereign AI foundation: controlled, evidenced, portable.

Stage 4 · Monthly

Fractional AI / Data CTO

Senior ownership of architecture, vendors, and roadmap.

Best for: Founders and operators who need an accountable technical leader for data and AI without a full-time hire.

What you get

• Architecture and platform ownership
• Vendor, model, and build-versus-buy decisions
• Roadmap and prioritization with leadership
• Implementation leadership across internal and external teams
• Board- and investor-ready reporting on AI posture

Outcome: Decisions made once, correctly, by someone who has shipped them before.

Stage 5 · Monthly

Managed AI Operations

Keep quality, cost, and governance where you left them.

Best for: Teams running AI workflows in production who need them evaluated, tuned, and reported on continuously.

What you get

• Scheduled evaluations and regression checks
• Prompt, model, policy, and data-mapping changes
• Agent reliability and cost monitoring
• Governance and outcome reporting
• Prioritized improvement backlog

Outcome: Systems that stay trustworthy after launch.