Atmobot

Atmobot · code & app review

We find what your app gets wrong — and we prove it.

A fixed-price review built for software that shipped fast: AI-generated code, backend-as-a-service apps, anything that got to market quicker than it got verified. Security and correctness both — every finding checked at the source and reproducible, not a scanner’s guess or an AI’s hunch.

Scanning…

The problem

Speed hides two kinds of holes. Neither shows up in the demo.

Security exposure

Where your data leaks

  • the public API key sitting in the browser bundle
  • access rules that don’t do what you think
  • database functions that quietly bypass your own policies
  • one customer’s data a single request from another’s

Common on Supabase, Firebase and other backend-as-a-service stacks — but not limited to them.

Correctness

Where your logic costs you

  • a payout path that can’t actually pay anyone
  • an admin action that changes a balance with no audit trail
  • two flags that both read “true” and disagree
  • the money math that’s off by one edge case

Most dangerous anywhere real money moves.

Either way, it surfaces when someone else finds it — and by then it’s a breach or a bad number, not a bug.

How it works

The model is the easy part. This is the part that’s hard.

Step 01

Map first, don’t guess

We build a map of your whole codebase before we review a line, so the work runs off the real structure — not off whatever happened to fit in a context window.

Step 02

Cover everything

We work through it in risk-tiered slices against a coverage ledger. Every file is accounted for. It’s a review of your system, not a sample of it.

Step 03

Prove every finding

Each issue is reproduced at the source before it makes the report. If we can’t reproduce it, it doesn’t ship — a short list of real problems, not a pile of maybes.

Step 04

A human owns the verdict

The tooling does the heavy lifting; a person decides what’s real and what actually matters to you.

Why not just use Claude, or a scanner?

Fair question. The honest answer is what makes this worth paying for.

vs. raw AI

Claude · ChatGPT · Copilot

We use frontier models too — that isn’t the moat. Used raw, an AI reviews only what fits in its window, has no memory of the rest of your codebase, and will confidently report bugs that aren’t real. The hard part is whole-codebase coverage and every finding proven.

vs. scanners

Snyk · SonarQube · Semgrep

They pattern-match known rules, so they miss the logic that actually costs you — the payout that can’t pay, the balance changed with no audit — and bury the few real issues under false positives you still have to triage. We read the actual logic and hand you a short, proven list.

vs. PR review bots

One diff at a time

Those look at one diff at a time, with no view of the whole system and no verification. We review the system, and we prove what we find.

The proof

Trust, not noise. Two recent reviews.

# review — fast-built app findings = 89 // all verified & reproducible critical = 40 // anon key in bundle, policy bypass, // tenant data one request away
# review — platform handling real money withdraw() → cannot pay out admin.setBalance() → no audit trail // correctness holes in the money paths, // each reproduced at the source

Both examples anonymized — no client named. Figures are from real reviews.

More than a one-time review

Three ways to put Atmobot on your code.

The review

A one-time, fixed-price read of your whole app. The front door.

from $2,500

Ongoing review

Atmobot as a standing set of eyes on every change — not a once-a-year audit.

scoped to you

Custom workflows

Verification tuned to your stack and your own rules, running on every change.

scoped to you

Every engagement is fixed price, scoped in writing before the first hour — no hourly surprises. Want the holes closed? Remediation is scoped the same fixed way.

Looking to map how work flows across your whole operation, not just the code? That’s our SDLC & operational audit — a broader engagement that sits alongside the code review.

Questions

Straight answers

Isn’t this just Claude reviewing my code?

We use frontier models, yes — but raw, they miss most of your codebase and invent findings. What you’re paying for is whole-codebase coverage and every finding proven. That’s the hard part, and it’s the whole product.

Do you only review security?

No — security and correctness both: the exposure that leaks data, and the logic bugs that cost money.

How is this different from a scanner?

Every finding is read and proven by a human, with reproduction steps. You get a short list of real issues, not a wall of false positives.

What do you need from me?

Read access to the codebase. That’s it to start.

Do you fix what you find?

Optionally, scoped the same fixed way after the review. Ongoing review is available too.

Better a bug than a breach

Send us your repo. We’ll tell you exactly where you stand.

Better a bug than a breach — or a bad number. Every finding proven at the source, a short list you can act on, and a fixed price agreed before the first hour.