Free playbook
The AI-Native Engagement Playbook.
How we scope, price, build, and run AI-native engagements. Same methodology we use with every cohort, published openly so you can evaluate the fit before paying a deposit.
Download the PDF
Get the full playbook as a PDF
Same content as below, optimized for offline reading and sharing with your team. Drop your email, get the PDF, plus ~1 email/month with new engagement patterns and case studies. No spam, unsubscribe anytime.
1. The 3-phase engagement model
Every engagement is broken into Discovery → Build → Run. You commit one phase at a time. No SaaS-style annual lock-in. No open-ended retainer.
Phase 1 · Weeks 1–2
Discovery
We map the workflow, the systems, the decisions, and the baseline metrics. Output: a scoped statement of work.
Phase 2 · Weeks 2–4
Design
We design the operating model: data access, retrieval, prompts, review queues, controls, and the KPI dashboard.
Phase 3 · Weeks 4–8
Build
We ship a production thin slice on real data, with versioned prompts, evaluation harness, and human review.
Phase 4 · Weeks 8+
Run
We run the workflow with you weekly, expand into adjacent work, and report against baseline.
2. Pricing by outcome cluster
We organize our work around 5 outcome clusters. Pricing is cluster-specific because the controls, integrations, and review burden differ.
| Cluster | Discovery | Build | Run / mo |
|---|---|---|---|
| Revenue & Growth | $5k | $15-22k | $2-3k opt |
| Operations & Throughput | $6k | $20-28k | $2.5-4k opt |
| Risk & Compliance | $8k | $30-40k | $4-6k opt |
| Customer Experience | $5k | $18-25k | $2-3k opt |
| Knowledge & Insight | $6k | $22-30k | $3-5k opt |
Discovery is the only commitment to start. Build is scoped after Discovery with a fixed price. Run is month-to-month, no lock-in.
3. Controls we ship
- · Versioned prompts with rollback history
- · Approved source list per workflow
- · Audit log on every input, output, model version, and reviewer action
- · Reviewer queues for low-confidence or high-impact cases
- · Labelled evaluation harness with weekly accuracy reports
- · Named owner for every high-risk decision
- · Quarterly risk attestation available on request
4. KPIs we report against
Every engagement is measured against operating metrics, not model benchmarks. We publish a weekly performance review during the Run phase comparing actuals to the baseline captured in Discovery.
- · Workflow throughput per operator
- · Cycle time (intake → output)
- · Quality consistency (error rate, rework)
- · Cost per transaction
- · Cluster-specific KPIs (revenue lifted, CSAT, false-positive rate, etc.)
5. What we hand over
- · Workflow map (intake → retrieval → generation → review → action)
- · Versioned prompt repository
- · Evaluation harness with labelled test set
- · Audit log infrastructure
- · Internal model card per prompt + model combination
- · Operating runbook (escalation paths, on-call, rollback)
- · Exit plan (what stays with you if the engagement ends)
6. What we don't do
- · No SaaS-style annual prepay or lock-in
- · No referral to third-party agencies — we deliver end-to-end
- · No vague AI strategy decks without an execution path
- · No wide-but-shallow prototypes that don't survive production volume
Ready to scope yours
Start an AI Project
The configurator scopes your workflow, picks the right package, and generates a fixed-price Statement of Work. Projects start at $15k.