THE LOGIC-READINESS AUDIT
Isolate undefined behavior before it creates review debt or ships to production.
AI made code cheap. It made verification expensive.
The Logic-Readiness Audit is a 72-hour diagnostic for CTOs, VPs of Engineering, and CPOs who need to know exactly where one high-risk product flow will force engineering into manual interpretation — or ship with undefined failure, recovery, or escalation behavior.
I audit the product logic before AI-assisted work reaches engineering review.
One flow. 72 hours. $1,200 fixed fee.
YOUR TEAM IS MOVING FAST. THE PRODUCT RULES ARE NOT.
AI-assisted teams can generate screens, flows, and implementation faster than ever. But generated work can look complete before the underlying product behavior is actually defined.
That is where rework has moved.
It no longer shows up only as mid-sprint clarification. It now shows up as review debt for engineering and unpredictable behavior for users. Senior engineers are forced to reconstruct product intent after the work already looks buildable.
On the execution side:
The spec leaves critical rules implicit. Retries are not fully defined. Permission rules are still assumed. Failed states are left for engineering to invent. Rollback behavior gets discovered during review. Work compiles — but misses the business rule.
This is not an engineering-speed problem. It is a product-logic verification problem.
On the user-facing side:
When exception paths and recovery behavior are not defined before build, the workflow ships with unpredictable behavior. AI tools confidently fill in the missing logic with plausible but invalid rules. Users encounter failure states nobody designed and correction paths nobody defined.
That is where trust starts breaking.
THE FLOW LOOKS READY BECAUSE THE MAIN PATH IS VISIBLE.
Most teams can see the intended flow.
The user starts here.
They submit this.
The system returns that.
That part is usually clear.
The risk lives in the edges.
What happens after partial failure?
Who owns the retry state?
What must the reviewer verify before implementation is accepted?
Which action is forbidden after submission?
What happens if AI fills in missing behavior with a plausible but invalid rule?
Internal teams often miss these gaps because the missing rules are buried in shared context. A senior engineer knows what "should" happen. A product lead sees the intended path. An AI tool confidently fills in the rest. The audit makes those hidden rules visible before they become review debt.
This is not a UX review.
This is not a design critique.
This is not code review.
This is not a strategy workshop.
It is a 72-hour audit of one feature, workflow, or sprint-risk area where undefined product behavior could create rework, review burden, or AI-assisted implementation risk.
You provide the current Figma files, prototypes, or flow diagrams alongside the relevant tickets, user stories, and specs. You bring the known engineering questions, review blockers, and a short note on the primary point of friction.
I map the flow for missing rules, undefined states, orphaned transitions, and reviewer-verification risk. The goal is simple: find where engineering will be forced to infer product behavior before the work is safe to build, review, or automate.
A FIXED-SCOPE DIAGNOSTIC ON ONE HIGH-RISK FLOW.
Think this is your flow?
Submit one high-risk workflow and I’ll confirm whether it fits the 72-hour audit
THREE ARTIFACTS AND ONE HANDOFF TO DIAGNOSE THE FLOW.
At the end of the audit, you receive an execution-facing diagnostic package built for product and engineering leaders.
1. Verification Burden Map
A marked-up flow map showing where the current product logic is not yet verifiable by engineering. It identifies undefined states and missing transitions, unclear ownership points, retry and recovery gaps, permission gaps, and the areas where generated work could look complete while missing the rule.
2. Requirements Risk Log
A filterable risk log documenting the highest-risk product-rule gaps. Each row identifies the exact flow location and missing rule, the likely engineering interpretation, the review or implementation risk, and the decision required before build can proceed safely. No theory — just the product rules your team needs to resolve.
3. AI Build-Risk Score
A one-page executive assessment of whether the current flow is safe for AI-assisted implementation. The score evaluates context density, missing rule exposure, reviewer burden, and the likelihood of AI-assisted guesswork. The output is a clear read: safe to proceed, proceed with constraints, or do not build until logic is resolved.
4. Handoff Call
A focused 45-minute call to walk through which rules are missing and what will create review burden. The call ends with one operating question: who is resolving these product rules before engineering has to?
Want this diagnostic on your flow?
Submit the workflow, delivery stage, and current risk signals. No internal files required at this stage.
THIS COSTS LESS THAN THE REVIEW DEBT IT PREVENTS.
The audit is a $1,200 fixed fee.
If one senior engineer loses a day reconstructing product intent from a ticket that looked ready, you are already close to the cost of the audit. If that review exposes a missing rollback rule, contested permission state, or undefined failure path, the rework that follows usually costs more.
The audit pays for itself when it prevents one review loop, one rewritten ticket, or one AI-assisted build that looked complete and missed the business rule.
You are not buying more design. You are buying a clearer answer to a more expensive question: can engineering verify this product behavior without reconstructing intent?
IF NO MATERIAL GAPS ARE FOUND, THE AUDIT IS FREE.
If the targeted flow contains no material product-logic gaps, you pay nothing. A material gap is an undefined rule, state, constraint, or edge condition likely to create review burden, implementation guesswork, or invalid AI-assisted behavior. If there is nothing meaningful to isolate, there is nothing to bill for.
THE AUDIT DIAGNOSES THE Gap. THE SPRINT FIXES IT.
The Logic-Readiness Audit identifies where the flow is exposed. It does not rewrite the full specification or produce the final Product Logic Verification Contract.
If the audit finds material gaps, the next step is the AI-Readiness Sprint.
The audit answers: where is this flow exposed?
The Sprint answers: what exactly must the system do instead?
BUILT FOR TEAMS MOVING FASTER THAN THEIR PRODUCT RULES CAN BE VERIFIED.
The Logic-Readiness Audit is a strong fit for CTOs, VPs of Engineering, and CPOs at AI-forward organizations — specifically teams preparing one high-risk flow for AI-assisted implementation where undefined behavior carries real downstream cost.
If your senior engineers are clarifying product behavior during review, if generated work looks complete but the rules are still unclear, or if users are encountering correction and recovery paths nobody explicitly designed — this audit is built for that problem.
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 to know whether one product flow is about to create review debt or ship with undefined behavior.
ISOLATE UNDEFINED BEHAVIOR BEFORE IT REACHES THE BUILD.
If you have a product flow that is moving toward engineering with behavior that is still undefined, this audit is designed to find the gaps before they become review debt.
One flow. 72 hours. $1,200 fixed fee.
If no material gaps are found, you pay nothing.