AI Pain-Point Prototype Sprint

Who this is for

Organizations that have decided AI should help with something specific, and have then discovered that the distance between that decision and a live system is longer than anyone expected.

• You have a workflow that is visibly costing time, risk or effort, and someone already owns it.
• A pilot exists, or a vendor demo impressed people, but nothing has reached production.
• Your engineers are willing but are being handed a mandate rather than a specification.
• Security and data governance need answering before anything is allowed near real records.

What happens

The sprint is the first three steps of the method applied to a single workflow, and then the handover written down.

1. Frame the pain point

   I sit with the people who own the problem and establish what it actually costs in time, risk and effort, what a solved version looks like, and how we would know it was solved. That becomes the acceptance criteria, and every later decision is measured against it.

2. Design against your constraints

   Data sources and integration points, the risk tier the use case belongs in, and the human-in-the-loop controls that tier requires. Governance is settled here rather than discovered during a security review later.

3. Build the working prototype

   I build it myself using AI coding tools, running against real documents and real data wherever your governance permits. Not a mockup and not a click-through. Something the people who own the pain point can put their hands on.

4. Specify the stack end to end

   Hardware and GPU sizing, operating systems, databases, security, data management, model routing and per-query cost metering, taken through to a procurement-ready bill of quantities where you need one.

5. Hand it over

   Your engineers receive a demonstrated system plus the specification that produced it. They are hardening something they have already seen work rather than interpreting a vision.

What you get

• A working prototype of the workflow, usable by the people who own it.
• A reference architecture and an end-to-end stack specification.
• A risk tier with the human-in-the-loop controls that tier requires.
• A cost model, metered per request rather than estimated per seat.
• Acceptance criteria the production system has to meet.
• A handoff package written for engineers, not for a steering committee.

Scope, timing and commercial terms are agreed per engagement. Nothing is published here, because the right shape depends on how many systems the workflow touches and how much of your data the prototype is allowed to see.

What this is not

It is not staff augmentation, and it does not end with production code written by me. I design the solution, build the prototype and specify the stack; your engineers take it to production, and I stay involved to direct the build and hold the technical bar. It is also not a strategy document. If what you need is a roadmap across many candidate use cases rather than one built thing, the Governed AI Readiness Assessment is the better starting point.

Why this works

Ten AI workflows reached production across nine business functions this way, every one of them shipping into a department that did not report to me, on a platform running since October 2024 at about $0.015 per query all-in. Each of those began exactly as this sprint does: one pain point, one prototype, one handover.

Read the case study

Start

A useful first message names the workflow, what it costs you, and who inside the organization owns it.