Install an AI delivery workflow your team can actually review.

Move one team, repo, or operating process from scattered prompting to a controlled request, spec, AI-assisted work, review, QA, and accepted-output workflow.

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Start with the Audit unless the Sprint scope is already clear.

Send enough context to decide the right paid next step. Detailed access, proof permission, delivery cadence, and sample inputs are handled only after fit is confirmed.

Choose Audit

You want the bottleneck map, first-workflow recommendation, and Sprint scope before committing.

Choose Sprint

You already know the workflow, have a decision owner, and can supply sample inputs and outputs this week.

No payment is collected on this page. Paid work starts after Audit checkout or Sprint invoice. No guaranteed speed, revenue, or headcount reduction.

Quick fit check

Five fields. No payment here. If there is a fit, the next reply is an Audit checkout, Sprint scope, or one clarifying question.

No calendar hold or paid work starts until the Audit checkout or Sprint invoice is accepted.

The problem

AI coding gets faster. Delivery control often gets weaker.

Teams adopt Codex, Claude Code, Cursor, Copilot, and local agents, then lose work inside chat threads, inconsistent specs, loose review gates, unclear handoffs, and unreliable QA habits.

The Sprint does not promise model-specific magic. It installs operational discipline around one real workflow so useful AI-assisted work becomes inspectable, repeatable, and easier to accept.

Scope

One workflow. One accountable owner. One accepted run.

01

Map the current workflow

Inputs, outputs, bottlenecks, decision points, access expectations, and current delivery cadence.

02

Install operating instructions

Repo or process instructions, work-item template, decision gates, and handoff records.

03

Run a real workflow

One representative work item passes through request, spec, AI-assisted work, review, QA, and acceptance.

Best fit

Built for small technical teams already using AI tools.

Good fit

  • AI/dev agencies with 3-20 people shipping client work.
  • Founder-led B2B SaaS teams with 1-10 engineers.
  • Consultants or small technical teams using Codex, Claude Code, Cursor, Copilot, or similar tools.
  • Teams with visible delivery pressure, messy workflow, or repeated handoff needs.

Not this Sprint

  • Enterprise AI transformation.
  • Broad org redesign.
  • Hands-off autonomous agent teams.
  • Regulated or mission-critical workflows without a named risk owner, approvals, and rollback path.

Proof of work

Based on real operating discipline, not generic AI strategy.

Traceable delivery

Requests, specs, decisions, review notes, QA evidence, and handoffs are captured so work is easier to inspect.

QA before acceptance

Outputs are checked through build/test evidence, release notes, and explicit accept-or-revise gates.

Proof boundaries

Client proof is anonymized by default and only strengthened with permission after paid work proceeds.