Varyence

Varyence is a vibe coding security audit service in the same category as Beesoul, but with a sharper focus: security above everything else. Where Beesoul covers the full picture of an AI-generated codebase — architecture, scalability, maintainability, and security — Varyence specifically targets the security surface. This is a meaningful distinction when your product handles data that has real consequences if compromised: payment information, health data, personally identifiable information, or anything that creates legal exposure.

What they’re good at

The security audit focus means they’re looking at things general code reviewers often deprioritize: authentication and session handling, injection vulnerabilities in AI-generated database queries, exposed secrets and credentials in code or version control, authorization logic errors (the AI built auth, but did it build the right permission model?), and third-party dependency vulnerabilities.

AI-generated code has specific security failure patterns that are different from traditionally-written code. The LLM often implements something that looks correct and passes basic review but has a subtle vulnerability in an edge case. Varyence is specifically tuned to find these.

What they’re not good at

If you want a comprehensive technical assessment of your codebase — architecture concerns, scalability ceilings, code maintainability, development velocity impact — Varyence isn’t the right tool. Their specialty is narrow. For the full-picture audit, pair them with a general code review or consider Beesoul instead.

They also don’t do remediation. Like Beesoul, the output is an audit report, not a repaired codebase. You’ll need a developer or another agency to act on their findings.

How they price

Fixed-price audit engagements starting around $2,000 for smaller, single-surface products and up to $8,000 for complex multi-service backends with significant data handling. The price reflects the scope of the security surface being reviewed, not the physical size of the codebase.

Where they fit in your stack

The trigger moment is clear: before you launch to a significant user base when your product handles sensitive data. Running a security audit at this point is one of the highest-leverage uses of a few thousand dollars available to a seed-stage founder. The cost of a security incident — regulatory, reputational, and customer-trust — is orders of magnitude higher than the cost of finding the vulnerability beforehand.

The honest take

Security audits for AI-generated code are a category that didn’t exist two years ago and now matters a lot. The pattern of non-technical founders shipping apps that handle real user data using tools they don’t fully understand is creating real risk. Varyence is doing necessary work. For any founder shipping a product with meaningful data handling, spending a few thousand dollars on a security review before scaling is one of the clearer decisions in this directory.