AI DELIVERY GOVERNANCE
Prevent undefined product behavior from entering the roadmap.
AI made code cheap. It made verification expensive.
When your backlog keeps sending incomplete product logic into engineering, the problem is no longer one risky flow. It is a delivery pattern.
AI Delivery Governance is an ongoing fractional engagement for CTOs, VPs of Engineering, and CPOs who need high-risk product work hardened into verification-ready specs before it reaches engineering or AI-assisted implementation.
$9,000 / month. Async-first delivery.
Currently accepting 1 new embedded partner.
YOUR BACKLOG IS SHIPPING UNDEFINED BEHAVIOR.
A single unclear ticket creates rework. A backlog full of unclear tickets creates a system.
When upcoming work enters engineering with implied behavior, missing edge states, unclear permissions, or undefined exception paths, the cost moves downstream.
On the execution side:
Senior engineers end up reconstructing product intent during review. Tickets bounce back. Implementation slows down. Review becomes manual triage instead of validation.
On the user-facing side:
AI-assisted tools fill in missing behavior with plausible but invalid rules. Users experience workflows that behave unpredictably under stress — especially when the model is uncertain, incomplete, incorrect, delayed, or needs escalation. That is where trust starts breaking.
This is the pattern: undefined product behavior moving downstream until engineering, users, or production absorbs it.
Product owns roadmap movement.
Design owns the user flow.
Engineering owns implementation.
QA owns defect detection.
AI tools generate the output.
But the rules underneath the flow often stay implied — retries and timeouts, permission escalations, failed states, rollback behavior, approval and handoff logic, reviewer acceptance criteria. Those rules are assumed until they become expensive.
You do not need more meetings. You need a lightweight product-logic layer that keeps high-risk work review-ready and behaviorally defined before it reaches execution.
NO ONE OWNS PRODUCT LOGIC VERIFICATION.
Seeing this pattern across the backlog?
If undefined behavior keeps reaching engineering review, request a capacity review to determine whether the issue is one blocked flow or a recurring governance gap.
ONGOING BACKLOG HARDENING FOR AI-ASSISTED DELIVERY.
This is not general UX support.
This is not visual design capacity.
This is not code review.
This is not QA.
I integrate into your existing workflow — Jira, Linear, Figma, Slack, Notion, or your current stack — and harden upcoming tickets before they create review debt or ship undefined behavior.
The focus is narrow: find where ambiguity is likely to create engineering drag or user-trust failure, and define the product rules before engineering or AI has to infer them.
A LIGHTWEIGHT WEEKLY CADENCE.
Each week runs the same three-step sequence.
A Review-Readiness Scan identifies upcoming tickets likely to create review debt, rework, or unpredictable AI behavior. Priority goes to work involving permissions, approvals, retries, failed states, rollback behavior, integrations, data-state transitions, and AI exception paths.
Product-Logic Hardening defines the missing behavior engineering needs before implementation begins — edge-case responses, forbidden actions, permission boundaries, recovery paths, handoff rules, escalation paths, and reviewer acceptance checks.
Everything is delivered directly into the artifacts your team already uses: tickets, specs, flows, acceptance criteria, edge-case matrices, and reviewer checklists. No parallel documentation system. No process theater.
I WILL NOT BECOME THE BOTTLENECK.
Two commitments, in writing:
Any high-risk ticket flagged in the weekly Review-Readiness Scan and confirmed in scope will receive verification-ready product logic at least 48 hours before sprint planning. If my delivery delays a planned sprint ticket, that week's fee is credited against the following month.
If your Engineering Lead does not see a material reduction in product-logic clarification loops on hardened tickets by the end of Month 1, you can end the engagement with zero penalty.
FOUR RECURRING DELIVERABLES.
AI Review Readiness Queue.
A prioritized queue of upcoming tickets likely to create review debt or unpredictable AI behavior if product rules stay undefined.
Verification-Ready Ticket Updates.
High-risk tickets receive defined product logic, explicit constraints, and reviewer acceptance checks before sprint planning.
Continuous Edge-Case Matrix.
A shared, living reference for failure states, handoffs, recovery paths, user-facing states, backend expectations, and escalation rules.
Monthly Review Debt and Logic Risk Report.
A leadership summary showing which ambiguity patterns were found, hardened, or escalated — and what it protected downstream.
Want this layer across your backlog?
Request a capacity review and I’ll confirm whether your team has the recurring ambiguity pattern this engagement is built to address.
THIS COSTS LESS THAN THE PATTERN IT BREAKS.
The engagement is a $9,000 / month flat retainer.
At typical senior engineering rates, that is roughly 45 hours of engineering time. A single sprint cycle where two senior engineers spend three days clarifying behavior that should have been defined before build — debating edge cases, bouncing tickets, rewriting generated logic — likely costs more than the retainer. That is before QA churn, delayed release, support escalations, or the downstream cost of an AI workflow that shipped with undefined failure behavior.
You are not buying more design capacity. You are buying a product-logic layer that protects engineering velocity and reduces the risk of unpredictable AI product behavior reaching users.
THE SPRINT FIXES ONE FLOW. GOVERNANCE PROTECTS THE BACKLOG.
Use the AI-Readiness Sprint when one specific flow is blocked, unstable, or unsafe to hand to engineering. Use AI Delivery Governance when the same pattern keeps appearing across the roadmap.
The Sprint answers: what exactly must this flow do?
Governance answers: how do we stop undefined product behavior from reaching engineering every week?
BUILT FOR TEAMS WHERE INCOMPLETE LOGIC IS A RECURRING COST.
AI Delivery Governance is a strong fit for CTOs, VPs of Engineering, and CPOs at AI-forward organizations where high-velocity delivery has outpaced the product-logic layer underneath it — fintech, healthcare, data-ingestion, compliance, and AI-agent workflows where undefined behavior at scale carries compounding downstream cost.
If your team is reopening shipped tickets to answer "what happens if..." questions on a regular basis, if senior engineers are clarifying product behavior during review instead of verifying implementation, or if AI-assisted tools are filling logic gaps with behavior nobody explicitly defined, this engagement is built for that pattern.
This is not general design support.
It is not code review.
It is not broad strategy consulting.
It is focused specifically on product-logic quality, review readiness, and AI execution risk reduction.
DETERMINE THE RIGHT INTERVENTION BEFORE YOU COMMIT.
Book a 15-minute Product Logic Sync. We will look at your current sprint queue, identify where product-logic ambiguity is likely to create review burden or unpredictable behavior, and determine whether the right next step is a 72-hour Logic-Readiness Audit, a 5-day AI-Readiness Sprint, or ongoing AI Delivery Governance.
$9,000 / month. Async-first delivery.
Submit your current delivery context, backlog pattern, and review-friction signals. I’ll determine whether the right next step is a Logic-Readiness Audit, AI-Readiness Sprint, or ongoing AI Delivery Governance.