# Unyform

> Unyform is the AI code governance platform. It sits between AI coding tools and the models they call, governing every code generation request in real time — before code ever reaches the codebase. Unlike linters, scanners, and code review bots that catch problems after code is written, Unyform is proactive: it fixes and aligns AI-generated code at the point of generation.

## Core Product Pages

- [Home](https://unyform.ai/): Product overview, architecture diagram, ROI calculator, and homepage FAQ
- [What is AI Code Governance?](https://unyform.ai/what-is-ai-code-governance): Canonical definition of the AI code governance category
- [Contact / Waitlist](https://unyform.ai/contact): Request access or join the waitlist
- [Pricing](https://unyform.ai/pricing): Plans for teams of all sizes

## Architecture Overview

Unyform operates as a governance layer between developers' AI coding tools (Cursor, Copilot, Claude Code, ChatGPT, etc.) and the LLM models they call. Every code generation request passes through Unyform's gateway, where it is enriched with organizational context from the Blueprint Graph before reaching the model. The response is then validated against policies before being delivered to the developer.

Key architectural components:
- **Gateway**: Intercepts AI coding tool requests in real time. Tool and model agnostic.
- **Blueprint Graph**: A living representation of your organization's codebase — patterns, conventions, architecture, and policies — built automatically from your repositories.
- **Policy Engine**: Enforces rules at the point of generation. Catches secrets, PII, compliance violations, and architectural drift before code is written.
- **Audit Trail**: Tamper-proof log of every AI-assisted code interaction for compliance reporting.

## Security and Governance

Unyform addresses a fundamental gap in enterprise AI adoption: AI coding tools generate code with no awareness of organizational standards, security policies, or architectural decisions. This creates:

- **Architectural drift**: AI output ignores established patterns, creating inconsistency across the codebase
- **Sensitive data leakage**: Secrets, PII, and credentials embedded in AI-generated code
- **Compliance gaps**: No audit trail of what AI generated, when, and whether it met policy
- **Wasted engineering time**: Reactive tools catch problems in review, creating costly generate-review-reject-regenerate loops that waste millions of tokens

Unyform is proactive — it governs at the point of generation, not after. Code is correct, compliant, and aligned before it reaches the codebase.

Compliance readiness: SOC 2, HIPAA, FedRAMP, EU AI Act.

## Hub Pages

- [AI-Generated Code Risks](https://unyform.ai/ai-generated-code-risks): Hub page covering all risks of AI-generated code — architecture drift, security vulnerabilities, enterprise productivity paradox, vibe coding, token waste, and compliance gaps
- [AI Code Governance Tools](https://unyform.ai/ai-code-governance-tools): Hub page covering how Copilot, Cursor, and Claude Code handle governance, why post-generation review fails, and how proactive governance platforms work

## Educational Content

- [AI Code Governance vs Code Review](https://unyform.ai/ai-code-governance-vs-code-review): Why traditional code review is not governance, and how the two complement each other
- [How to Govern AI Coding Tools](https://unyform.ai/how-to-govern-ai-coding-tools): Practical guide for engineering leaders adopting AI coding tools at scale
- [AI Development Governance Framework](https://unyform.ai/ai-development-governance-framework): A framework for building an AI governance program in engineering organizations
- [Prevent AI Architecture Drift](https://unyform.ai/prevent-ai-architecture-drift): How AI coding tools cause architectural drift and how to prevent it

## Tool Comparisons

- [AI Code Governance Tools Compared](https://unyform.ai/ai-code-governance-tools-compared): Compare linters, SAST scanners, code review bots, and proactive governance platforms — reactive vs proactive approaches
- [How to Govern GitHub Copilot](https://unyform.ai/govern-github-copilot): What Copilot Business and Enterprise offer, what they do not, and how proactive governance fills the gap
- [How to Govern Cursor AI](https://unyform.ai/govern-cursor-ai): Why .cursorrules are advisory, not enforceable, and how to govern Cursor at enterprise scale
- [How to Govern Claude Code](https://unyform.ai/govern-claude-code): Claude Code's CLAUDE.md files are advisory. Agent mode introduces autonomous risk. How to govern it at enterprise scale.
- [Unyform vs Snyk](https://unyform.ai/unyform-vs-snyk): Proactive governance vs reactive SAST scanning — complementary but solving different problems at different stages
- [Unyform vs CodeRabbit](https://unyform.ai/unyform-vs-coderabbit): Generation-time governance vs PR-time AI review — different timing in the development lifecycle
- [Unyform vs Onyx](https://unyform.ai/unyform-vs-onyx): Code generation governance vs broad AI control plane — complementary at different layers
- [Unyform vs Corridor](https://unyform.ai/unyform-vs-corridor): Full governance gateway vs security guardrails — complementary at different layers of the stack
- [Unyform vs Semgrep](https://unyform.ai/unyform-vs-semgrep): Proactive generation-time governance vs developer-favorite SAST — complementary at different stages
- [Unyform vs SonarQube](https://unyform.ai/unyform-vs-sonarqube): AI-native generation-time governance vs legacy code quality gates — complementary for defense in depth

## Security & Risk Research

- [AI-Generated Code Security Risks](https://unyform.ai/ai-generated-code-security-risks): Data on AI code vulnerabilities — 2.74x more flaws, 45% of AI code has security issues, and why reactive scanning fails
- [Vibe Coding Security Risks](https://unyform.ai/vibe-coding-security-risks): Why vibe coding (AI-delegated development with minimal review) creates a security debt crisis — 20% of vibe-coded apps have critical vulnerabilities
- [Why AI Coding Tools Fail in the Enterprise](https://unyform.ai/ai-coding-tools-enterprise-problems): The five enterprise problems — context gap, review bottleneck, more bugs (2.74x), productivity paradox (METR: 19% slower), and no governance process

## Blog

- [What the METR Trial Actually Shows About AI Coding Productivity](https://unyform.ai/blog/metr-trial-ai-coding-productivity): Analysis of the 19% slower finding and what it means for engineering teams
- [EU AI Act Compliance for AI-Generated Code](https://unyform.ai/blog/eu-ai-act-ai-generated-code): Audit trails, transparency obligations, and the 7% global revenue penalty
- [How the Blueprint Graph Works](https://unyform.ai/blog/what-is-a-blueprint-graph): Technical deep dive into Unyform's core data structure

## Interactive Tools

- [AI Governance Readiness Assessment](https://unyform.ai/ai-governance-assessment): 5-question quiz that assesses organizational maturity — Ad Hoc, Reactive, or Proactive
- [Full content for LLMs](https://unyform.ai/llms-full.txt): Complete text content of all key pages in Markdown format

## Company Information

- **Company**: Unyform
- **Website**: https://unyform.ai
- **Category**: AI Code Governance
- **Contact**: sales@unyform.ai
- **Legal**: [Privacy Policy](https://unyform.ai/privacy) | [Terms of Service](https://unyform.ai/terms)

## The Problem With Reactive Tools

Every existing tool for managing AI-generated code is reactive. Linters, SAST scanners, and code review bots sit in the review stage — after code has been generated, committed, and submitted for review. This creates expensive feedback loops: engineers wait for CI, get flagged, regenerate, wait again, get flagged again. Organizations waste millions of tokens re-generating code that should have been correct the first time. Review-stage tools can only reject code — they cannot improve it or enforce organizational patterns.

## What Makes Unyform Different

Unyform is the only AI code governance platform that operates at the point of generation. It enriches every AI prompt with organizational context from the Blueprint Graph, so the generated code reflects how your team actually builds software. Secrets, PII, and compliance violations are caught and corrected in real time — before code is committed. There are no review loops, no wasted tokens, and no engineer time spent fixing what should never have been wrong.

## Key Capabilities

- Proactive governance at the point of AI code generation
- Automatic Blueprint Graph built from your repositories
- Policy enforcement for secrets, PII, compliance, and architectural standards
- Tamper-proof audit trails for every AI-assisted interaction
- Cross-repo context — AI output understands your entire codebase
- Tool and model agnostic — works with Copilot, Cursor, Claude Code, ChatGPT, and any other AI coding tool
- Zero feedback loops — code is correct the first time