Vantys

Vantys

What Vantys Is

Vantys is a system of record for human decisions about AI-produced code.

When someone approves AI-generated code for production, Vantys records:

  • Who made the approval
  • Under which policy
  • At what point in time
  • With what context (PRs, discussions, tickets)

It doesn't interpret the decision. It doesn't prevent bad code. It preserves the fact that the decision occurred.

Think of it like:

  • Accounting software — records transactions, doesn't judge them
  • Flight data recorders — captures what happened, doesn't prevent crashes
  • Git — tracks changes, doesn't review quality

Auditors remain auditors. Security teams remain accountable.

Vantys makes the answer to "show me" exist.

The Problem

When an auditor or enterprise customer asks: "How was this AI-generated code reviewed and approved?"

Teams currently have to manually search through:

  • GitHub PRs
  • Slack conversations
  • Jira tickets
  • Policy documents

And piece together a narrative after the fact. Often the context is lost or incomplete.

The scenario:

An auditor asks: "I see GitHub Copilot was used to write parts of your authentication system. Who approved this code? What policy governs AI-generated code reviews? Where's the documentation?"

Your team scrambles to find:

  • The PR approval in GitHub
  • The Slack discussion about the approach
  • The Jira ticket context
  • Evidence that your policy was followed

By the time you assemble this, hours have been spent — and the story is still incomplete.

Why Now?

AI-assisted coding tools like GitHub Copilot and Cursor are now standard at fast-growing B2B companies. But compliance frameworks and enterprise security reviews haven't caught up yet.

We're seeing this play out in three ways:

1. SOC 2 auditors are starting to ask

Companies going through their first or second SOC 2 audit are getting questions like "how do you review AI-generated code?" and "what controls exist for code written by AI tools?" Teams are scrambling to piece together answers.

2. Enterprise customers are questioning it

During vendor security reviews, enterprise buyers are asking: "Do you use AI to write code? How is it reviewed?" Companies with good answers are moving faster through sales cycles.

3. The tooling exists, but the audit trail doesn't

Teams use GitHub for reviews, Slack for discussions, Jira for tracking — but there's no single place that shows "this AI-generated code was approved by [person] under [policy] with [context]." When asked, teams manually search through multiple tools.

How It Works

Vantys connects to your existing tools and creates structured records when AI-generated code is approved:

1

Connect your tools

  • •Read-only access to GitHub, Slack, Jira
  • •We only ingest metadata, not your actual code or full messages
2

Declare your policies

  • •Define what approval means at your company
  • •Set requirements for AI-generated code reviews
3

Generate records on demand

  • •When you need an audit artifact, request it through the dashboard
  • •Vantys assembles the evidence: who approved, under what policy, with what context
4

Present to auditors/customers

  • •Structured records in both JSON (machine-readable) and PDF (human-readable)
  • •Complete provenance chain showing the approval process

You don't change your process. Vantys documents what's already happening.

Why I'm Reaching Out

I'm talking to security and engineering leaders at B2B companies to understand:

  • Are you actually being asked about AI code governance by auditors or customers?
  • What's your current process when this comes up?
  • Is this painful enough to warrant a solution, or just a minor inconvenience?

If this resonates, I'd love to hear your experience.

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