Catch Bugs Before They Reach Production
Type errors, logic bugs, security vulnerabilities, and policy violations — CodeChecks uses two AI models in sequence to surface issues that linters and single-model tools miss.
What CodeChecks detects
Beyond syntax. Beyond linting. AI-powered analysis that understands intent, context, and your project's specific conventions.
Type errors
Mismatched types, null dereferences, and incorrect interface implementations that TypeScript alone might miss — especially across AI-generated boundaries.
Logic bugs
Off-by-one errors, inverted conditions, incorrect edge-case handling, and algorithmic mistakes that look syntactically correct but behave wrong at runtime.
Security vulnerabilities
SQL injection vectors, unvalidated user input reaching sensitive APIs, missing auth checks, and exposed secrets — caught before the code reaches your repository.
Policy violations
AI models sometimes touch files they shouldn't — env configs, auth modules, payment code. CodeChecks policy flags these automatically and blocks unsafe changes.
Architecture mismatches
Code that works in isolation but breaks your conventions — wrong import paths, duplicated abstractions, missed dependency injection, or inconsistent error handling patterns.
Regression risks
Changes that break existing behaviour identified through cross-model verification. The Verifier checks the patch against stated requirements, not just syntax.
How AI bug detection works in CodeChecks
Bug detection isn't a single step — it's built into every phase of the pipeline.
Requirements-first planning
The Planner reads your requirements and repo context before any code is written. This means the Implementer knows what success looks like — reducing speculative code that introduces bugs.
Policy-gated implementation
The Implementer's diff is checked against your policy rules before delivery. Files containing env vars, auth logic, or payment handling are locked unless you explicitly allow them.
Cross-model verification
GPT-4o reviews the diff independently from the implementation model. Model disagreements surface the highest-risk changes for human attention.
Two AI models — double the bug-catching power
Every CodeChecks run uses two independent AI models. Claude (Anthropic) plans and implements. GPT-4o (OpenAI) verifies the result with a structured JSON verdict.
The two models have different training data, different architectures, and different failure modes. A bug that one model normalises as acceptable is often flagged by the other — giving you a higher-confidence verdict than any single-model tool can provide.
Try it freePlanner + Implementer
Claude (Anthropic)
Verifier
GPT-4o (OpenAI)
AI bug detection — common questions
Can AI really detect bugs better than a senior engineer?
Not always — but it never gets tired, never skips the tedious parts, and never has a bad day. AI bug detection is most valuable as a first-pass gate that catches mechanical and pattern-based issues so human reviewers can focus on the parts that require judgement.
Does CodeChecks run tests for me?
CodeChecks verifies the diff and its alignment with requirements — it doesn't replace your test suite. Think of it as the layer between code generation and CI: it catches issues before your tests even run.
What if the bug is in business logic I haven't described?
That's what human review is for. CodeChecks is excellent at mechanical, structural, and policy-level bug detection. Business-logic correctness still requires a human who understands the domain.
How does two-model verification help with bug detection?
Each model has different failure modes. Claude excels at planning and implementation coherence; GPT-4o brings an independent structured JSON verdict. A bug that slips past one model is often caught by the other.
Stop shipping bugs. Start verifying.
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