Methodology, not abstractions.

Four phases. Pick the one that fits.

Most clients enter through one of four engagement shapes. The diagnostic is the typical front door. The sprint and rescue are scoped implementations. The retainer is what runs after.

• // 01

  Operations Diagnostic — 2 weeks, $7,500

  Workflow audit, opportunity ranking, and a written recommendation. The right starting point when you're not sure where AI fits or where to spend.

• // 02

  Workflow Sprint — 3 weeks, $12,000

  Scoped, single-workflow implementation. Right when the team has clarity on what they want and just needs it built.

• // 03

  Pilot Rescue — 4 weeks, $18,500

  Diagnose what failed and rebuild. Right when an existing pilot didn't ship the way it was supposed to.

• // 04

  Fractional AI Engineering — $4,500–$15,000 / month

  Ongoing operations after deployment. Right when the system is live and the team needs senior AI engineering on retainer.

1 — Operations Diagnostic.

Two weeks. $7,500. Before recommending anything, we understand what's actually happening.

Week 1 — Discovery. We talk to the people doing the work, not just the project sponsor. We watch the workflow. We read the existing documentation. We pull the data the system would need to use.

Specific deliverables: a written summary of the current workflow, the people involved, the systems they use, the volume and velocity of work, and the time spent on each step.

Week 2 — Recommendation. We write a structured recommendation document. Five sections: what we observed, what the highest-value AI opportunity is, what's required to do it well, what it would cost (engagement plus ongoing), and what could go wrong.

The deliverable is eight to fifteen pages. It's specific. It's yours to keep regardless of whether you continue with us.

2 — Workflow Sprint.

Three weeks. $12,000. A scoped, single-workflow implementation.

Week 1 — Design and integration setup. We confirm the workflow with end users (one half-day workshop). We set up access to the systems we'll integrate with. We provision the cloud resources. We build the first end-to-end skeleton — the AI works on test data, integrated with the real systems.

Week 2 — Build and tune. We build the production implementation. We construct an evaluation suite from real historical data. We tune until quality thresholds are met. We instrument cost and quality monitoring.

Week 3 — Pilot, train, hand off. We run a one-week shadow-mode pilot. The AI runs in production but its output isn't acted on. We compare to what the team actually does. We tune based on the gap. We train the team. We hand off the runbook.

End state. A working system, an evaluation suite, monitoring dashboards, a runbook, and trained operators.

3 — Pilot Rescue.

Four weeks. $18,500. When there's an existing pilot that didn't ship the way it was supposed to. The diagnostic-and-rebuild pattern.

Week 1 — Diagnose. We audit what was built. We identify which of the seven failure modes apply (see Why AI Pilots Fail). We write a recovery plan.

Weeks 2–3 — Rebuild. Whatever needs to be rebuilt — evaluation system, prompt framework, integration layer, monitoring — gets rebuilt. We re-use what's worth re-using.

Week 4 — Re-pilot. Shadow mode for one week. Tune. Ship.

End state. A system that actually does what the original pilot was supposed to do, plus the operational discipline that was missing the first time.

4 — Fractional AI Engineering.

$4,500–$15,000 per month, ongoing. The retainer that runs after the initial engagement.

Monthly check-ins, prompt updates as the business changes, model upgrades as providers ship new ones, evaluation suite maintenance, monitoring oversight, and incident response.

Most clients move into a retainer within sixty days of going live, because AI systems quietly drift if nobody is watching them.

How we choose cloud and model providers.

We are deliberately not cloud-specific. We build production systems on Microsoft Azure, Google Cloud, and AWS. We use Anthropic Claude, OpenAI GPT, Google Gemini, and open-source models depending on the task.

The right cloud and model choice for any given engagement depends on five things:

• Where the client's data already lives
• What compliance requirements apply (HIPAA, SOC 2, FedRAMP, GDPR, etc.)
• What the existing tech stack looks like
• What the team can operate after we leave
• What the workload economics require

We make this choice transparently in the diagnostic phase, with the trade-offs written down. We're not paid by any provider to recommend one over another.

What every engagement includes.

Regardless of phase, every engagement includes:

An evaluation system. A scored test suite running on real historical data, with quality thresholds defined up front. Without this, "is it working?" has no answer.

Production-grade integration. The AI lives inside the tools the team already uses. No copy-paste workflows.

Cost and quality monitoring. Dashboards from day one. The CFO knows what each transaction costs. The operations lead knows whether quality is holding.

A runbook. A written document the client owns forever, describing how to operate, monitor, update, and troubleshoot the system. Includes how to call us for help when needed.

Training sessions. Real training, not a 30-minute demo. Operators, managers, and technical caretakers each get their own session.

What we don't do.

We don't do strategy decks without implementation. We don't do AI training programs. We don't do reseller arrangements for someone else's software. We don't do "AI readiness assessments" that take six months and produce a PowerPoint.

We build, ship, and operate.