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Executive Summary

Developers at mid-size to enterprise companies want to use AI coding assistants like ChatGPT, Claude, and Cursor on their full codebases, but security and compliance teams block them because copy-pasting proprietary code into third-party chat interfaces leaks IP and often includes hardcoded secrets, API keys, or PII. The problem hits hardest at companies with 50-500 engineers where security policies exist but aren't yet enforced by sophisticated tooling, forcing developers into either shadow AI usage or tedious manual sanitization that breaks their flow. GitGuardian found over 10 million secrets leaked in public repos, and the same exposure risk exists when developers feed unsanitized code to AI tools. The MVP would be a CLI tool or IDE extension that scans a codebase, redacts secrets and sensitive patterns using configurable rules, then packages the sanitized code into optimized context files formatted for specific AI assistants (respecting their token limits and context window structures). It would generate a manifest showing what was redacted and provide restore instructions so developers can manually reinsert sensitive values after getting AI suggestions. The smallest credible version targets Python and JavaScript repos, integrates with GitHub, and costs $15-25 per developer per month with a free tier for repos under 50 files, aiming to land initial sales through bottom-up adoption by engineering teams who already pay for Cursor or Copilot. This market looks attractive now because context windows reaching 200K+ tokens finally make whole-repository reasoning practical, shifting AI assistants from autocomplete tools to architecture advisors that need full codebase context to deliver value. The $4.8B sizing assumes 12 million professional developers would pay around $400 annually for tooling that unlocks safe AI usage, which is conservative given Cursor already charges $20/month and GitHub Copilot has crossed 1 million paid seats. The timing works because companies are writing AI usage policies right now and need enforcement tools, not just guidelines, and developers have proven they will pay $20-40/month for AI coding tools that measurably speed up their work. The main differentiation is focusing on the sanitization and context preparation layer rather than building another AI assistant, positioning this as the secure ingestion pipeline that works with any LLM. Competitors like GitGuardian focus on secret detection in commits, not pre-AI-usage sanitization, while current workarounds involve manual redaction or blanket bans that developers ignore. To validate, interview 15-20 engineering managers at Series A to Series C startups about current AI usage policies and whether a $20/seat tool would get budget approval, then build a basic Python-only scanner and try to get five design partners using it weekly within their teams. The biggest risk is that this becomes a feature inside existing AI assistants rather than a standalone product, which you would test by seeing if early users value it enough to pay separately or if they expect Cursor or GitHub to build it for free.

Executive Summary

Why Now

Frontier LLM context windows crossed 200K tokens in late 2023 (Claude 2.1, GPT-4 Turbo), making whole-codebase reasoning feasible for the first time but exposing secrets-leakage and context-overflow pain at scale. GitHub reported 1M+ Copilot paid seats by mid-2023, training developers to expect AI assistance but not yet solving the repo-level analysis handoff. Remote-first engineering (post-2020) amplified async collaboration via chat-based AI tools, increasing the frequency of codebase sharing outside secure IDEs. SOC 2 and data residency requirements now block copy-paste workflows at 40%+ of series A+ startups, creating budget for sanctioned tooling.

Pain Points

Developers manually copy-pasting code into ChatGPT or Claude accidentally leak API keys, tokens, and credentials embedded in config files or environment variables, creating security incidents.

Pasting entire repos exceeds the 200K token context window of frontier models, causing truncation that omits critical dependencies or forces developers to manually select files, slowing iteration.

Flat file dumps lose directory structure, import paths, and cross-file dependencies, forcing the AI to guess module relationships and producing incorrect refactoring suggestions.

Secure context-aware codebase ingestion for AI coding assistants

Executive Summary

Secure context-aware codebase ingestion for AI coding assistants

Executive Summary

Executive Summary

Why Now

Pain Points

MVP Preview

Market Validation

Market Opportunity

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Validation Questions

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