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Step 2 - Codebase Context and Learning (Validation): Add vector database ingestion to index the entire codebase, documentation, and past PRs. Use retrieval-augmented generation (RAG) to inject relevant context into the LLM prompt, so reviews reference existing patterns, architectural decisions, and team conventions. Implement feedback loops: when developers accept or reject suggestions, use that signal to fine-tune the model or adjust prompts. Expand to 3-5 languages and add security vulnerability detection using OWASP and CWE databases. Raise pricing to $299/month for teams up to 20 engineers. Goal is 50+ paying teams and 70%+ comment acceptance rate, proving the AI provides senior-level insights. This takes 3-4 months.

Step 3 - Team-Specific Fine-Tuning and Multi-Repo (Growth): Build infrastructure to fine-tune smaller, faster models (Llama 3, Mistral, CodeLlama) on each customer's codebase and review history. This creates a personalized code review agent that knows the team's standards, common pitfalls, and architectural patterns. Add multi-repo support and organization-level dashboards showing code quality trends, review velocity, and top issues. Integrate with Slack and Linear for notifications and issue creation. Expand to enterprise teams (50-200 engineers) at $999-2999/month. Partner with YC companies and fast-growing startups for case studies. Goal is $100K MRR and clear differentiation from GitHub Copilot (which does suggestions, not review) and static analysis tools (which lack context). This takes 6-9 months.

Step 4 - Platform and Moat (Dominance): Expand beyond PR review to become the AI engineering teammate. Add features like automated test generation, performance profiling suggestions, architecture refactor proposals, and onboarding documentation generation. Build a marketplace where teams can share custom review rules and fine-tuned models. Integrate with CI/CD to block merges on critical issues and provide compliance reports for SOC2/ISO27001. Offer an on-premise or VPC deployment option for enterprises with strict data residency requirements. The moat is the trained model and integration depth: after 6-12 months, the AI knows the codebase better than any human and becomes irreplaceable. Pricing scales to $5K-20K/month for large enterprises. Goal is $1M+ ARR and clear path to acquisition by GitHub, GitLab, or a DevSecOps platform, or continued growth as an independent AI-native developer tools company.