Preventing codebase drift between AI sessions
Your engineers are shipping more code, faster, with AI in the loop. The challenge appears between sessions, where silent breaking changes and context loss accumulate. Open each one below to see what it actually looks like on the ground.
AI modifies a core interface, silently breaking downstream dependencies across the project.
A development session ends at a context limit, leaving the AI with zero memory of where work stopped or why.
Tests get removed, documentation falls behind, and technical complexity creeps in unnoticed until production breaks.
The TRACE framework provides the engineering controls that catch these breakdowns before code reaches production.
Automated coherence checks at every session boundary
TRACE relies on five core checks to verify AI codebase coherence:
Truth Anchoring
Establishes one authoritative source per concept. When an anchor changes, all consumer dependencies update automatically.
Registry Enforcement
Ensures documentation remains a verified, executable artifact rather than an outdated afterthought.
Automated Verification
Implements tiered testing that expands over time — ensuring test suites never shrink during AI generation.
Controlled Evolution
Enforces complexity thresholds auto-calibrated to your specific repository parameters.
Execution Contracts
Mandates explicit preconditions and postconditions for every change type.
Lightweight, language-agnostic, and MCP-native
Built as a single 53 KB compressed binary with zero dependencies, TRACE integrates seamlessly into existing developer workflows:
System Compatibility
Language-agnostic (TypeScript, Python, Go, Java, Rust), tool-agnostic (Cursor, Claude Code, GitHub Copilot), and CI-agnostic (GitHub Actions, GitLab CI, Jenkins).
MCP Integration
Functions as a native MCP server tool, allowing AI assistants to perform automated software integrity checks without human intervention.
Open-Source Repository
Access the complete documentation on the TRACE Kit site and review the source code in the TRACE GitHub repository.
Embedded verification and team workshops
During an AI Delivery engagement, our forward-deployed engineers configure TRACE directly on your repository. Every prompt and build gate is instrumented for AI code verification, leaving behind a fully monitored environment for your internal team.
For engineering organizations already using AI coding tools, we offer a dedicated 2-day onsite TRACE Workshop to help staff engineers and tech leads establish architectural controls across their repositories.
Local execution with zero telemetry
TRACE executes locally with zero network calls and zero data collection. It analyzes repository structure rather than file contents, ensuring GDPR compliance, alignment with SOC2 data boundary requirements, and ISO 27001 integrity verification out of the box.
Govern your AI-generated codebase
Schedule an Onsite Workshop
Book a 2-day technical training session for your staff engineers and tech leads to implement TRACE across your repositories.