THE AI-READINESS SPRINT
Lock one high-risk AI workflow into verification ready product logic.
A 5-day remediation sprint for teams with one AI workflow too ambiguous to hand safely to engineering or an AI coding tool.
I resolve the undefined states, constraints, fallback paths, escalation rules, and acceptance checks so your team can build, review, and verify the flow without guessing product intent.
One flow. 5 business days. $4,800 fixed fee.
If I miss an in-scope behavioral constraint during your build, I fix it at no extra cost.YOUR SPEC LOOKS COMPLETE. THE BEHAVIOR IS NOT.
High-speed tools and compressed timelines push teams to define the visible path first.
The screen looks done. The ticket looks ready. The prototype looks complete.
But the system behavior underneath is still unresolved.
What happens when the AI is uncertain?
What happens when the data is incomplete?
What happens when an API fails?
What happens when the user needs to correct the output?
What happens when the workflow must stop, roll back, or escalate?
When those rules are missing, the cost shows up in two places.
On the execution side:
Your review queue gets heavier. Senior engineers spend sprint time resolving edge cases, defining API failure states, debating permissions, and correcting generated work that compiles but misses the business rule.
They are not just reviewing implementation. They are reconstructing product intent.
On the user-facing side:
Trust breaks faster. Users do not need AI workflows to be perfect. They need the product to define what happens when the system is uncertain, incomplete, incorrect, delayed, or needs escalation. If exception paths, recovery behavior, and correction rules are not defined before build, the workflow ships with unpredictable behavior.
That is where trust starts breaking.
You are paying execution talent to invent rules they should have been handed before build started.
YOU CANNOT CLEANLY RESOLVE THE GAP UNDER SPRINT PRESSURE.
The same shared assumptions that helped the team move quickly are often what left the critical constraints undefined in the first place. If the team had the spare capacity to step back and harden the logic cleanly, the ticket would not be stalled now.
Pulling a PM or product designer off the active sprint to retroactively define behavior usually creates a second bottleneck instead of removing the first.
The AI-Readiness Sprint gives you a dedicated intervention on one blocked flow without disrupting the rest of the roadmap.
A FIXED-SCOPE REMEDIATION ON ONE BLOCKED FLOW.
This is not a redesign.
This is not an open-ended consulting engagement.
This is not a UX audit.
This is not code review.
It is a 5-day sprint to remove ambiguity from one critical feature, workflow, or bottleneck that is currently slowing engineering execution or risking user trust.
You provide the current Figma files, prototypes, or flow diagrams alongside the relevant Jira tickets, user stories, and specs. You bring the known engineering questions, review blockers, and the specific failure points or unclear behavior slowing the work.
I define the missing states, map the required system behavior, and hand back a verification-ready package your team can build against.
Have one flow stuck in this state?
Submit the workflow, delivery stage, and current blocker. I’ll confirm whether it fits the 5-day Sprint.
THREE ARTIFACTS AND ONE HANDOFF TO UNBLOCK THE FLOW.
At the end of Day 5, you receive a concrete package your team can use to verify AI-assisted generation or unblock manual implementation.
1. Product Logic Verification Contract
A structured, reviewer-verifiable rule set defining the required behavior, constraints, transitions, and acceptance checks for the target flow. It covers the core invariants the system must not violate, strict state definitions and triggers, backend and permission expectations, escalation rules, reviewer acceptance checks, and an AI execution block to constrain coding tools from inventing missing logic.
2. Edge-Case Response Matrix
A filterable matrix mapping each critical failure state to a definitive system response. Each row defines the triggering event, the required backend state change, the exact UI response, the user recovery path, and the rollback or escalation rule.
3. Hardened Flow Assets
Updated Figma files reflecting the resolved logic. These are not aesthetic revisions. They are visual updates required to support correct system behavior — error states, status messaging, timeout handling, rollback behavior, and escalation flows made visible.
4. Engineering Handoff
A 60-minute review with your Engineering Lead and PM to walk through the package, align on reviewer acceptance checks, and unblock implementation.
Need this package for your flow?
Submit the blocked workflow and I’ll confirm whether it is scoped tightly enough for the Sprint.
WHAT A VERIFICATION-READY RESOLUTION LOOKS LIKE.
Example: an AI-assisted CSV data importer
The ambiguity
The design has a simple "Cancel" button. But the spec never defines what happens if the network drops on row 5,000 of 10,000. If that gap reaches implementation, AI or engineering may generate a UI cancel action without a backend rollback rule. The interface looks fine. The database risk remains undefined.
The resolution
The Sprint converts that undefined behavior into buildable logic.
Instead of a vague cancel action, the flow gets a strict "all or nothing" transaction rule — partial data cannot be committed.
Instead of leaving the backend response open to interpretation, the failure state gets a defined rollback sequence. Instead of showing a generic UI state, the product shows a locked recovery state: "Safely removing 5,000 rows from staging..."
Instead of asking reviewers to infer intent from the interface, the acceptance check tells them exactly what must be true before the work can pass.
The engineer inherits a rule. The reviewer has a check. Nobody invents behavior at 11pm.
BUILT FOR TEAMS STALLED BY INCOMPLETE LOGIC DURING EXECUTION.
The AI-Readiness Sprint is a strong fit for CTOs, VPs of Engineering, and CPOs at AI-forward organizations — specifically teams relying on AI coding tools that need strict behavioral constraints before implementation moves forward.
It is designed for teams blocked by one high-stakes feature or workflow: fintech, healthcare, data-ingestion, compliance, and AI-agent workflows where undefined behavior carries real downstream cost.
If your team has shipped a ticket only to reopen it two weeks later to answer "what happens if..." questions, this sprint exists for that problem.
If you have already run the Logic-Readiness Audit and need immediate remediation, this is the next step.
This is not for teams that need visual polish.
It is not for code review.
It is not for broad strategy consulting.
It is for teams that need one risky flow turned into verification-ready product logic before build moves forward.
THIS COSTS LESS THAN THE REWORK IT PREVENTS.
The Sprint is a $4,800 fixed fee.
At typical senior engineering rates, that is less than the cost of a few days spent debating unresolved behavior in review, rewriting generated logic, or reopening a shipped ticket. If two senior engineers lose two days clarifying behavior that should have been defined before build, you have likely already spent more than the Sprint — and that is before the downstream cost of delayed release, QA churn, support escalations, or production fixes.
The Sprint pays for itself when it prevents one mid-build logic rewrite, one unstable AI workflow from shipping, or one senior engineering pod from spending another week reconstructing product intent.
IF I MISS AN IN-SCOPE BEHAVIORAL CONSTRAINT, I FIX IT AT NO EXTRA COST.
The Product Logic Verification Contract is designed to account for the material edge cases identified in scope.
If your engineering team finds an in-scope behavioral constraint I missed during implementation, I update the Contract and related specs at no extra cost. This applies to the selected flow, artifacts provided at kickoff, and implementation questions raised within 14 days of handoff.
THE SPRINT FIXES ONE FLOW. GOVERNANCE PROTECTS THE BACKLOG.
The AI-Readiness Sprint is a focused intervention to lock the logic on one specific flow and unblock an immediate implementation path. If the issue is not one blocked flow but a repeated pattern of undefined behavior across the roadmap, this Sprint will fix the immediate fire.
Ongoing AI Delivery Governance is a fractional engagement for teams with recurring logic gaps across the backlog — where multiple features need logic hardening before they enter engineering or AI-assisted implementation.
SEND THE BLOCKED FLOW. GET A VERIFICATION-READY PACKAGE IN 5 DAYS.
If you have a high-stakes feature that is unstable, ambiguous, or unsafe to hand to engineering or an AI coding tool, this sprint is designed to lock the logic fast.
One flow. 5 business days. $4,800 fixed fee.
Guarantee: If I miss an in-scope behavioral constraint during your build, I fix it at no extra cost.