AI Workflow Setup Sprint · bounded implementation
Build a better way to get one important job done.
We work with the people who know the job, build a focused AI system, and test it against real work. Your team learns to use it, review it, and keep improving it. Scope and success criteria are agreed before we build.
Commercial shape
$7,500
Timing
Scoped to the smallest meaningful implementation
Boundary
One validated workflow inside one company.
Larger builds continue as Managed AI Execution or a Custom AI System, quoted separately.
Find the right starting point
Bring the audit, workflow map, or equivalent evidence. We will pressure-test whether the workflow is ready for implementation and whether the proposed V1 is small enough to prove safely.
§ 01The engagement
What you leave with
Included
- Final workflow and product boundary with explicit out-of-scope conditions
- Architecture, systems, data, permissions, source, and integration plan
- Working implementation against representative inputs and edge cases
- Evaluations, acceptance tests, human approvals, and exception handling
- Deployment, observability, documentation, and named ownership
- Training, SOP changes, rollout plan, and early adoption review
- Evidence readout and recommendation to scale, revise, pause, or stop
Not included
- An unlimited feature backlog or open-ended transformation program
- A production promise before access, dependencies, and acceptance tests are known
- Autonomous high-impact actions without human authority and incident handling
- A handoff that leaves the operating team with software nobody owns or understands
§ 02How the work moves
How does the work move?
Phase 01
Confirm the boundary
Validate the workflow, inputs, systems, owners, value baseline, trust boundary, acceptance tests, dependencies, and V1 scope before build capacity is committed.
Phase 02
Build and evaluate
Implement the smallest useful loop, test representative and adversarial cases, expose uncertainty, instrument the system, and keep high-impact decisions with people.
Phase 03
Deploy and learn
Release into the real workflow, train the owner and users, monitor usage and exceptions, compare evidence to the baseline, and decide the next gate.
Who this is for
Is this the right fit for us?
A good starting point
- A completed audit or equivalent decision package
- A named executive sponsor, workflow owner, and adoption owner
- Representative data, documents, systems access, and edge cases
- Written acceptance tests and a natural human-review boundary
- A bounded V1 with a measurable decision at the end
When another path fits better
- A broad request to automate a department without a selected workflow
- No access to representative inputs or the people doing the work
- A deadline that requires pretending dependencies or testing do not exist
- An expectation that AI replaces accountability, review, or workflow ownership
FAQ
What operators ask about AI Workflow Setup Sprint.
01Do we have to complete an AI Opportunity Audit first?
Not always, but the workflow must already have the same evidence: current-state map, baseline, owner, access, system boundary, acceptance tests, risk constraints, and a real implementation decision. If those are missing, the audit is the safer and cheaper starting point.
02How long is a sprint?
The timeline is scoped to the smallest meaningful implementation and depends on systems, data, integrations, review requirements, and acceptance tests. Sprinter does not advertise a fixed delivery time before those constraints are known.
03What does 'working system' mean?
A system connected to representative inputs and the real workflow, evaluated against written acceptance criteria, observable after deployment, documented, and owned by someone on your team. A disconnected demo does not qualify.
04Who owns the IP?
The contract defines ownership, client-specific data and logic, third-party components, and Sprinter's reusable frameworks. No reuse or productization right is assumed from marketing copy.
05What happens if the sprint fails an evidence gate?
The work pauses, revises, narrows, or stops. The decision log records why. A sprint is designed to reduce risk, not force a production launch regardless of evidence.
Next step
Bring the work you want to make better.
We’ll work through the opportunity, the people involved, and a practical path forward. You don’t need a finished specification to start the conversation.
Need practical AI help? Start with Sprinter's $2,500 Executive AI Accelerator — one executive, two private calls, and three repeatable workflows. Review the executive accelerator →