After watching developers struggle with 200ms+ vector database queries for context retrieval, we realized RAG was fundamentally backwards. Why compute expensive embeddings when you can find context in 0.3ms with SMT-powered reasoning?

ContextLite uses SMT (Satisfiability Modulo Theories) solvers + BM25 heuristics to mathematically prove optimal context matches instead of guessing with similarity scores. We process 2,406 files/second with formal verification - understanding imports, dependencies, and code relationships that vector embeddings completely miss.

No GPU required, no embedding models, no vector databases. Just blazing fast context that actually reasons about your codebase structure using constraint satisfaction and theorem proving.

We're live in production with npm, PyPI, VS Code marketplace, and 8 other package managers. 14-day SMT trial with full formal reasoning, then $99 lifetime license. Enterprise teams get advanced analytics, multi-repo support, and custom deployment options.

The future of AI context isn't more computation – it's mathematical precision. SMT solvers can prove correctness; vector databases can only guess similarity.

Try the math: contextlite.com/downloads