Looks fine file by file. Fails as a system.
Each file passes review. The failures live in the seams between modules, requests, and schema versions — exactly where a file-level pass never looks.
AI Code Cleanup Production Hardening
AI Shipped the Prototype. We Ship Production.
When AI-built code buckles under real users, we make it survivable — audited, refactored, and hardened into architecture your team can actually operate. We do not replace AI. We clean up what speed leaves behind.
One week, fixed scope · Risk map and refactor roadmap · No rewrite required
Fixed-scope AI Code Health Audit with concrete deliverables
Auth, migrations, query performance, and review-process hardening
Staged refactors that keep your product shippable
The Code Passes the Glance Test. Not the Production Test.
AI made it cheap to add code, so the hidden costs moved downstream — to reviewers, on-call engineers, and the founder fielding support tickets. These are the symptoms teams describe right before they call us.
Ships fast. Verifies slowly.
AI moved generation upstream and pushed the cost downstream, onto reviewers who now spend more effort confirming plausible-looking code than they ever did writing it.
Worked in the demo. Breaks in production.
The happy path is where generated code is strongest. Real users arrive with edge cases, concurrency, and messy data the demo never exercised.
Auth done four different ways.
Authorization re-implemented per endpoint, slightly differently each time. The inconsistency is the vulnerability.
Migrations bolted on after launch.
Columns added once customers existed, with no backfill, so half your rows quietly violate the assumptions the code now depends on.
Tests edited to pass, not code fixed.
A green suite that asserts the buggy output, or mocks the path that actually breaks. Confidence with nothing underneath it.
Typing speed is not systems thinking. AI accelerates local output; architecture, verification, and operational safety still need human judgment — paid for now, or later as technical debt.
AI Accelerates Output. We Restore System Integrity.
We are both the firefighter and the structural engineer: fast on the urgent failures, deliberate on the architecture underneath, so the same fire does not start twice.
Architecture cleanup
We consolidate duplicated logic, draw real boundaries, and replace confusing abstractions with structure your team can hold in their heads.
Production hardening
Error paths, failure handling, alerting, and the operational basics that turn a demo into a system that survives real users.
Security & auth review
We centralize authorization, close the leaks that come from inconsistent checks, and review the trust boundaries AI tends to fumble.
Performance & query remediation
N+1 queries, missing indexes, and table scans that pass review and only surface at scale — found by tracing requests, not reading files.
Not a tool vendor selling more speed, and not a deck-ware consultant. We work in the codebase, where the failures actually are.
Start With a Map, Not a Rewrite
Three stages, each a deliberate step. Most teams start with the audit because it is fixed-scope, low-risk, and produces the artifacts that make every later decision obvious.
Phase 01
AI Code Health Audit
One week, fixed scope. We map the system, find the failure modes, and rank them by blast radius. You leave with a risk register and a refactor roadmap — not a vague opinion.
Best for
Teams with an AI-built app that is misbehaving under real users.
Phase 02
Targeted Refactor Sprint
We stop the bleeding first, then work the roadmap in stages — urgent fixes before structural ones — so the product stays shippable the entire time.
Best for
Teams who now know what is broken and need it fixed without a rewrite.
Phase 03
Reliability Retainer
Ongoing hardening, review discipline, and fractional engineering leadership that keeps system integrity from eroding as the codebase grows.
Best for
Teams modernizing over time who want an adult in the room.
A "Finished" SaaS, Hardened in Stages
A composite of real engagements: an AI-built product that demoed flawlessly and broke at a few hundred users. None of the bugs were exotic — each was locally plausible code failing at a system boundary.
Dashboard load
Before
81 queries, frequent timeouts
After
3 batched queries, sub-second
Authorization
Before
4 implementations, cross-account leak
After
1 shared helper, leak closed
Billing job
Before
Silently failing on NULL rows
After
Backfilled, de-mocked, alerted
Test suite
Before
Green, but asserting bugs
After
Behavior-tested, failure cases covered
Inherited an AI Codebase Nobody Can Fully Explain?
That is the normal starting point, not a failure. We make the mess legible: what it does, where it is fragile, and what to fix first.
How We Turn a Mess Into a Map
The sequence is deliberately diagnostic: gather ground truth, trace reality, map the system, rank the risk, and separate what is on fire from what can wait.
01
Pull the ground truth
Schema, migration history, error logs, slowest endpoints, auth paths, and the shape of the test suite. Half the signal is in what is missing.
02
Trace one request end to end
Follow a single important action through every layer and count the database round-trips. One trace surfaces more than a day of reading files.
03
Map the system
Architecture, data flows, and the boundaries where modules, requests, and schema versions meet — because that is where the failures live.
04
Build the risk register
Every finding rated by blast radius and likelihood, with file references, so prioritization is grounded instead of argued.
05
Separate urgent from structural
Identify the ten percent that is on fire and the rest that can be staged. You almost never need a rewrite — you need a sequence.
06
Sequence the refactor
A roadmap ordered so each step is reviewable and the product keeps shipping. We can execute it, or hand it to your team.
"In the agency era, founders paid later to fix cheap offshore code. In the AI era, they pay later to fix cheap generated code. The source of the code changed. The economics of cleanup did not."
This is pattern recognition, not nostalgia. The same structural engineering that productionized the last generation of cheap code is what AI-built systems need now.
Not a Rewrite. Not Raw Dev Hours.
The leverage is in hardening what exists, on a schedule, without freezing the product. That is a different service from "build it again" or "send us tickets."
Vincere
In-house
Freelancers
Agencies
Cost
Fixed-scope audit
$180K+/yr hire
Variable
$$$ retainer
Time to Start
Days
3–6 months
2–6 weeks
2–4 weeks
AI-Code Experience
Specialized
Depends on hire
Rare
Mixed
Approach
Staged hardening
Rewrite risk
Patch by patch
Rebuild pitch
Primary Output
Risk map + roadmap
Tribal knowledge
Bug fixes
New codebase
Time to Value
One week
Months
Variable
Weeks
Make Your AI-Built App Survive Production
One week, fixed scope. We map the system, rank the risks, and hand you a refactor roadmap that separates the fires from the structural work. No rewrite, no lecture about using AI.
Book an AI Code Health Audit ->
One week - Risk map and roadmap - Relief, not shame
Frequently Asked Questions
What is an AI Code Health Audit?
A fixed-scope, one-week review of an AI-built or speed-built codebase. We map the architecture, trace real requests, and inspect auth, data lifecycle, query performance, error handling, and test integrity. You receive a system map, a prioritized risk register, a production-hardening plan, and a sequenced refactor roadmap.
Do you rewrite the codebase?
Almost never. Rewrites freeze the product and trade known bugs for unknown ones. We stop the bleeding first, then work a staged refactor — urgent fixes before structural ones — so the system stays shippable the entire time.
Our app was built mostly with AI. Is that a problem?
No, and it is extremely common. AI is genuinely good at getting to a working prototype. The failure modes we fix — inconsistent auth, late migrations, N+1 queries, tests that pass for the wrong reason — are structural and repetitive, which is exactly why they are findable and fixable on a schedule.
What do we actually get from the audit?
Artifacts, not opinions: a system map of the current architecture, a risk register rated by blast radius and likelihood, an inventory of duplicated or unsafe patterns with file references, a production-hardening plan, and a refactor roadmap that separates urgent fixes from structural work.
Can you work with our existing engineering team?
Yes. We can run the audit and hand your team the roadmap, own the refactor ourselves, or embed alongside your engineers and accelerate where they lack bandwidth. The deliverables are designed to be executed by whoever owns the code next.
How long does the audit take and how fast can you start?
The audit is one week from kickoff. Onboarding needs read access to the repository and, ideally, production logs and schema. Most engagements start within days of scoping.
Do you only fix AI-generated code?
No. The same failure modes appear in any speed-heavy codebase where generation outran system thinking. AI accelerates how fast weak architecture ships, but the cleanup economics are the same as they have always been.
What does it cost?
The audit is fixed-scope, so the entry cost is known up front and low-risk. Refactor and retainer work is scoped from the roadmap the audit produces, so you decide what to fund with a risk-ranked plan in hand rather than a blank check.