Looks fine file by file. Fails as a system.
The bugs live where modules, requests, and schema versions meet. Nobody reviews that.
AI Code Cleanup Production Hardening
AI Shipped the Prototype. We Ship Production.
AI got you to a working product fast. Whether it holds up under real users is a different question. We find what is broken, fix it in stages, and keep you shipping.
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
Why AI-Built Apps Break in Production
AI made writing code cheap. Running it in production is still expensive. What founders tell us on the first call:
Review can't keep up with generation.
A feature takes an afternoon to generate and days to verify. Plausible-looking code is the hardest kind to review.
Worked in the demo. Breaks in production.
Real users bring edge cases, concurrent writes, and messy data the demo never saw.
Auth done four different ways.
Each endpoint re-implements authorization slightly differently. One of them has the hole.
Migrations bolted on after launch.
Columns added after customers existed, no backfill. Half your rows violate assumptions the code depends on.
Tests edited until they passed.
Green because it asserts the buggy output or mocks the path that fails. It proves nothing.
AI writes code faster than your team can verify it. You pay for engineering judgment up front, or you pay for it during an incident.
What We Fix
Four areas cover most of the damage. Urgent failures first, then the structure that produced them.
Architecture cleanup
We consolidate duplicated logic and replace confusing abstractions with boundaries your team can actually maintain.
Operational readiness
Environment separation, secret management, server security basics, and the monitoring that tells you something broke before your customers do.
Security & auth review
We centralize authorization, close the gaps left by inconsistent checks, and review the trust boundaries AI-generated code gets wrong most often.
Performance & query remediation
N+1 queries, missing indexes, and full table scans that pass code review and only show up under load. We find them by tracing real requests.
No slide decks. We work in your codebase and ship working code.
Three Ways to Work With Us
Start with the audit. Everything after it is scoped from what it finds, so you never commit to more than the next step.
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 get a risk register and a refactor roadmap, usable with or without us.
Best for
Teams with an AI-built app that is misbehaving under real users.
Phase 02
Targeted Refactor Sprint
Urgent fixes ship first. Structural work follows in reviewable stages. The product keeps shipping the entire time.
Best for
Teams that know what is broken and need it fixed without a rewrite.
Phase 03
Reliability Retainer
Ongoing code review, hardening, and fractional engineering leadership so quality holds as the codebase grows.
Best for
Teams that want senior engineering oversight while they keep shipping with AI.
A "Finished" SaaS, Hardened in Stages
A composite from real engagements: an AI-built product that demoed well and failed at a few hundred users. None of the bugs were exotic. All of them were found in one week.
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?
Most clients start there. The audit tells you what the system does, where it is fragile, and what to fix first.
How the Audit Works
Six steps. Ground truth first, judgment second, priorities last.
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 seams between modules, requests, and schema versions. That is where the failures live.
04
Build the risk register
Every finding rated by blast radius and likelihood, with file references.
05
Separate urgent from structural
About ten percent needs immediate attention. The rest can be staged. Rewrites almost never make the list.
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."
We did this work when the cheap code came from agencies. The failure modes have not changed. Only the author has.
How We Compare to Your Other Options
If the goal is making the codebase you already have production-ready without freezing the product, here is how the options compare.
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 the roadmap.
Book an AI Code Health Audit ->
One week - Fixed scope - Risk register and roadmap
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 fix the urgent problems first, then work through structural changes in stages, so the system stays shippable throughout.
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?
Concrete deliverables: 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.