Every Guardrail Checks the AI. Check Checks the Code.
Published 22 June 2026There are dozens of AI guardrail products. They check whether the AI is safe, whether the output matches a format, whether the response contains toxic content. None of them check whether the AI's code actually matches your project.
The landscape
The AI safety and guardrail space has exploded. Here's what exists:
Security firewalls (AEGIS, Pipelock, LlamaFirewall) — these check whether AI actions are safe. They look for prompt injection, unauthorized access, data exfiltration. They answer: "Is this AI doing something dangerous?"
LLM guardrails (Guardrails AI, Lakera Guard, NeMo Guardrails) — these validate AI output format and content. They check for toxicity, PII exposure, schema compliance. They answer: "Is this AI output well-formed?"
Eval platforms (Galileo, Braintrust, Maxim AI) — these score AI output after the fact. They measure accuracy, hallucination rates, quality. They answer: "How good was this AI output?"
The gap
None of these answer the question that matters most for AI coding agents: "Does this code match the developer's actual project?"
A security firewall doesn't know whether import redis from "express-redis-cache" is installed in your project. A guardrail tool doesn't check whether db.findUserByEmail() exists in your codebase. An eval platform scores the code after it's already been written and executed.
The code correctness problem — verifying that AI-generated code refers to real dependencies, real functions, and real project structure — is a completely different axis from safety, format, or quality scoring.
| Check | Security firewalls | LLM guardrails | Eval platforms | |
|---|---|---|---|---|
| Checks code correctness | Yes | No | No | No |
| Knows your project | Yes | No | No | No |
| Pre-execution | Yes | Yes | Varies | No |
| Post-write verification | Yes | No | No | No |
| No AI inside | Yes | Varies | Uses LLMs | Uses LLMs |
| Per-check pricing | $0.0068 AUD | Free (self-hosted) | Enterprise | SaaS |
Different axis, different product
Check doesn't compete with security firewalls. You should use both. A security firewall stops your AI from doing dangerous things. Check stops your AI from doing wrong things.
The difference matters. A command can be perfectly safe and completely wrong. npm install express-redis-cache is a safe command — it installs a public npm package. It's also wrong if you don't need that package and your AI hallucinated the import that requires it.
Check reads your actual dependencies, your actual source files, your actual project state. It verifies AI output against reality. No AI inside — deterministic verification. Same project, same output, same verdict, every time.
120 free checks. Install in 30 seconds.
$0.0068 AUD per check. No subscription. Credits never expire.
Get startedFrequently asked questions
Is Check a replacement for AI guardrails?
No. Check solves a different problem. Guardrails validate AI behavior and output format. Check validates whether the AI's code matches your actual project — real imports, real functions, real dependencies. Use both.
Why doesn't Check use AI?
Verification should be deterministic. If your package.json doesn't list a dependency, that's a fact — not something a model needs to reason about. Check reads your project state and compares. No probability, no hallucination risk, no model drift.
What does "checks the code" mean?
Check verifies imports against your package.json, function calls against your exported symbols, and commands against your system before execution. It answers one question: does this code match the developer's actual project?