Sita

Here are some FAQ: How is Sita different from traditional code search tools? Unlike tools like Sourcegraph that provide powerful search for senior developers who know exactly what to look for, Sita delivers guided, code-anchored context with citations specifically designed for AI agents and developers still learning your codebase. We don't just return search results; we create task-scoped context packs that minimize token usage while maximizing accuracy and relevance. What makes Sita better than static codebase mapping tools? While codebase mapping tools like CodeSee are excellent at creating visual representations of code architecture, Sita transforms that understanding into actionable, cited context packs that agents and developers can use immediately. We tie code to documentation, conversations, and tickets at the function and file level, auto-refresh documentation from commits and pull requests, and expose everything through MCP tools that integrate directly into your development workflow. How does Sita compare to traditional RAG solutions for code? Most RAG-for-code solutions miss the core problem of cognitive load and cost control. Instead of indexing everything and hoping for the best, Sita focuses on pruning, packaging, and prompting. We turn sprawling knowledge bases into minimal, explainable inputs with clear provenance and guardrails, ensuring your AI agents get exactly what they need without burning through tokens on irrelevant context. Will Sita still be valuable as AI coding tools get smarter? Absolutely. As tools like Cursor and Claude Code evolve, Sita becomes even more valuable as the context and governance layer that feeds these agents. We handle what AI editors won't: generating and maintaining internal documentation from code evolution, managing cross-platform connectors with proper access controls, and providing the deep search and dependency-aware context that makes these tools actually understand your specific codebase and coding standards. How quickly can we expect to see results after implementing Sita? Most teams see immediate improvements in their AI agent accuracy and reduced token usage within the first week. Developer onboarding time typically drops from months to weeks, with new engineers becoming productive 3-4x faster. You'll notice cleaner code suggestions that actually match your patterns, fewer failed attempts at fixes, and a dramatic reduction in time spent copying and pasting between documentation, Slack, and code.