Why current search fails you
Embedding-based search achieves <20% recall on Google DeepMind's benchmark. Most of your documents are invisible.
Vector search misses 80%+ of relevant documents. It finds text that looks similar, not text that answers your question.
Neither embeddings nor graphs explain why documents were retrieved. You can't verify or trust the results.
Intelligent exploration, not similarity matching
Dexrag uses Monte Carlo exploration to navigate your documents like a human expert would — and learns your domain automatically.
Explores documents like AlphaGo explores moves. Finds the right answers by intelligently navigating document structure.
Gets smarter with every query. Learns your domain-specific terminology and patterns automatically.
See why each document was found. No black box scores — understand the exploration path behind every result.
Built for documents that matter
Find exact clauses, not "similar sounding" text. Learns your contract patterns over time.
Knowledge bases that learn from ticket patterns. Reduces support volume by finding the right answer every time.
User guides and manuals that understand context. Adapts to how your users ask questions.
The numbers speak for themselves
Ready to find what you've been missing?
Upload a document. Run a query. See the difference Monte Carlo makes.