Does Your AI-Generated Code Meet Production Standards?
A scored quality profile for the code AI is shipping into your repo right now — correctness, security, readability, testability, maintainability, and the deltas vs. human-written code.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
AI assistants are now writing meaningful portions of the code shipping into production at most engineering orgs — and most teams haven't asked the hard question of whether that code actually meets the standards they'd apply to anything else. The risk isn't usually a single dramatic failure; it's the quiet accumulation of plausible-looking code that compiles, passes shallow review, and degrades the codebase one PR at a time. An honest code-quality assessment looks past the productivity claims at whether AI-generated code holds up across the dimensions that matter — correctness, security, readability, testability, maintainability, and the tooling that's supposed to catch the misses.
This free AI code quality assessment scores your organization across six dimensions and returns a clear quality profile in about six minutes. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional CTO would bring to your first conversation, where the goal is raising the quality bar on AI-generated work without slowing the team down.
What the AI code quality assessment measures
Code quality isn't a single number — it's a profile. The assessment scores six dimensions independently so you can see exactly where your AI-generated code holds up and where it doesn't: Correctness & Failure Modes (does AI code actually do what's specified, or just compile and look right), Security Hygiene (do auth, secrets handling, and input validation hold up in AI output), Readability & Convention Adherence (is the code idiomatic in your codebase or generic-looking), Testability & Test Quality (does AI write tests that catch real bugs, or tests that pass tautologically), Maintainability Over Time (will the code still be understandable in six months), and Tooling & Quality Gates (do your linters, type checks, and CI catch the failure modes specific to AI-generated code). The final question maps the specific quality gaps you're carrying so the result points directly at what to fix first.
Why AI code quality matters now
Engineering teams have adopted AI coding assistants faster than any developer tool in the modern era — and most haven't raised the quality bar to match. The exposure shows up in three patterns: production bugs from plausible-but-wrong logic that looked reasonable in review, security regressions from insecure defaults and skipped validation, and maintenance debt from generic, non-idiomatic code that resists clean extension. None of this is an argument against AI assistants. It's an argument for treating their output with the same quality discipline you'd apply to any contributor — review checklists tuned to the failure modes, static analysis that blocks merge, test quality standards that apply equally, and codebase grounding that keeps generations on-convention. Done right, this turns into an advantage: your team can ship AI-assisted work faster than competitors who are still arguing about whether to trust the output.
What you get at the end
You'll see an overall AI code quality score, a band that describes where you stand (from Below the Production Bar through Exemplary), a per-dimension breakdown showing which quality areas need attention, and a map of the specific gaps you're carrying. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional CTO engagement would prioritize to close your highest-cost gaps without slowing your engineers down. No generic sales deck.
Frequently asked questions
What is AI code quality and why does it need its own assessment?
AI code quality describes whether the code AI assistants are generating actually meets the standards you'd apply to human-written code — across correctness, security, readability, testability, maintainability, and tooling. It needs its own assessment because AI assistants introduce failure modes humans don't (hallucinated APIs, plausible-but-wrong logic, tautological tests, generic non-idiomatic patterns), and traditional code-quality measures weren't designed to catch them.
How long does the assessment take?
About six minutes. It's 19 scored questions across six code-quality dimensions plus a final question that maps the specific gaps you're carrying. Your progress auto-saves, so you can leave and resume without losing answers.
Will raising the quality bar slow my engineers down?
Good quality discipline does the opposite. The aim is to build guardrails that match the failure modes — review checklists, type-safety enforcement, codebase grounding, test quality standards — so AI assistants produce production-ready code on the first pass instead of code that needs to be reworked, re-reviewed, or shipped with hidden debt. Mature quality discipline lets your team adopt AI-assisted work more aggressively, not less.
Who is this assessment for?
It's built for CTOs, VPs of Engineering, engineering managers, and founders at mid-market and growth-stage companies whose engineers are already using AI coding assistants — and who want a clear-eyed read on whether the code those assistants are shipping actually meets a production bar.
What happens after I get my score?
You'll see a full code-quality profile with per-dimension scores and a map of the gaps you're carrying. If you'd like, you can share a few details and receive a personalized video walkthrough explaining your results and what a fractional CTO would prioritize to close your highest-cost gaps without slowing your engineers down.