Five things AI-assisted code gets wrong
Not because the model is careless, but because it cannot see your whole system — and neither can a founder reading a diff at midnight.
Credentials in your code
API keys, database secrets and tokens committed to the repository. The single most common finding, and the one that costs the most.
Dependencies with known advisories
Your lockfile read against the public advisory database, with the exact version that fixes each one.
Missing permission checks
Endpoints that fetch a record by id and return it without confirming it belongs to the person asking.
Unsafe patterns
Raw HTML rendering, string-built SQL, cookies without the Secure flag, and other patterns with a long history of going wrong.
Reliability gaps
Swallowed errors and unawaited promises — the reason a webhook can report success while quietly doing nothing.
What we could not check
Every report names the files skipped, the languages unsupported and the scanners that ran short. Coverage you cannot see is not coverage.
Connect a repository. Read the report. Fix. Re-scan.
The first audit takes a couple of minutes. You do not install anything, and you do not give us write access.
Connect GitHub
Install our GitHub App and pick the repositories it may see. Read-only, and you choose the list.
Start an audit
Choose a branch. We fetch a snapshot into an isolated sandbox and run the scanners that fit your languages.
Read the report
Findings ordered by what matters, each with the file, the line, the evidence and what to do about it.
Fix and re-scan
Re-run against your fix branch. The report tells you which findings you closed and which are new.
We never run your code. Not once, not anywhere.
A code scanner that installs your dependencies to check them has already executed whatever those dependencies wanted to run. That is the supply-chain attack we exist to warn you about, so we refuse to perform it.
No dependency installation
We read your lockfile and check the exact versions it pins against the advisory database. Nothing is installed, so nothing gets to run.
Isolated, disposable sandboxes
Each scan runs in its own throwaway container with no network access, no credentials in its environment, and hard CPU, memory and time limits.
Your source is not kept
The snapshot is destroyed when the scan ends. We keep the findings and short evidence excerpts — never whole files, never an archive.
Read-only, least privilege
The GitHub App asks for read access to repository contents and nothing else. It cannot push, open pull requests, or change your settings.
What this is — and what it is not
It is
- An automated code-health and security review
- Evidence you can check yourself, line by line
- A prioritised list of what to fix first
- An honest account of what was not covered
It is not
- A penetration test
- A certification or compliance audit
- A guarantee that your code has no problems
- A substitute for a human review before you scale
Automated analysis finds classes of problems, not every problem. We report potential issues with a confidence level, and we mark the ones a person needs to judge. We will never tell you your code is secure, because no tool can honestly say that.
Two plans. Both start with a 3-day trial.
Solo for a founder shipping one or two codebases, Team for several. The trial needs a card and runs the full product; there is no free plan.
Find out before your users do
One repository, one audit, a few minutes. If it comes back clean, that is worth knowing too.
3-day trial · card required · read-only GitHub access