Stop missing the documents your users need
<20% recall
~80% recall
Embeddings fail at scale (<20% recall). GraphRAG requires expensive manual schemas. We use intelligent exploration that adapts automatically.
$ npm install dexragCurrent RAG approaches fail on quality
August 2025: Google DeepMind demonstrates fundamental mathematical limitations of embedding-based retrieval. State-of-the-art models achieve <20% recall on simple tasks.
Read the paper →Vector search achieves <20% accuracy on DeepMind LIMIT benchmark with 50K documents
Pre-trained on Reddit and Wikipedia, can't learn your domain-specific terminology
Embeddings treat isolated chunks equally, losing document hierarchy and structure
Requires expensive manual entity/relation design and tuning for each new domain
Re-extract and rebuild entire knowledge graph whenever documents change
Neither embeddings nor graphs explain why documents were retrieved—compliance nightmare
What if your RAG could solve all of these problems?
Probabilistic exploration beats similarity search
Explores documents like AlphaGo explores moves
Personalizes to YOUR users' patterns
Scales to billions without mathematical ceilings
Continuously improves with every query
See the decision tree behind every result
Learns industry-specific terminology and patterns
Not training from scratch. Optimizing what already works.
Break the embedding ceiling with probabilistic exploration
Dexrag replaces static vector lookup with adaptive Monte Carlo search
Unified intake
Upload files, sync APIs, stream logs
Document graph
Maps clauses into knowledge lattice
Monte Carlo rollouts
AlphaGo-style path sampling
Intelligent scoring
Probabilistic relevance weights
Adaptive memory
Learns from user signals
Explainable trail
Shows exploration tree
Actionable output
Ranked passages + citations
Unified intake
Upload files, sync APIs, stream logs
Document graph
Maps clauses into knowledge lattice
Monte Carlo rollouts
AlphaGo-style path sampling
Intelligent scoring
Probabilistic relevance weights
Adaptive memory
Learns from user signals
Explainable trail
Shows exploration tree
Actionable output
Ranked passages + citations
Monte Carlo keeps accuracy as datasets jump from 50K to billions of documents.
Learns firm-specific language by week four without new embeddings.
Adaptive maps resolve repeat questions before they escalate to agents.
Benchmarks don't lie
| Test | GPT-4 Embeddings | Dexrag (Day 1) | Dexrag (Day 30) |
|---|---|---|---|
| DeepMind LIMIT (50K docs) | 18% recall | 67% recall | 89% recall |
| Legal clause extraction | 100% baseline | 115% | 152% |
| Technical doc navigation | 100% baseline | 118% | 147% |
| Support ticket reduction | - | -18% | -42% |
| Infrastructure cost | $500/mo | $99/mo | $99/mo |
Monte Carlo search outperforms static embeddings from day one, with the gap widening over time as adaptive learning kicks in.
On Google DeepMind's LIMIT benchmark with 50K documents, Dexrag achieves 89% recall vs 18% for GPT-4 embeddings.
No expensive vector database infrastructure needed. Pay $99/mo instead of $500+/mo for Pinecone, Weaviate, or Qdrant.
Generic AI vs. Intelligence that knows YOU
Static embeddings return identical results regardless of user context or behavior
Each customer gets results optimized for their unique patterns and terminology
Pre-trained on Reddit, Wikipedia—generic knowledge that doesn't fit your domain
Adapts to industry-specific terminology, abbreviations, and document structures
Embedding recall degrades as document count grows—mathematical ceiling at 250M docs
47% better by day 30. No retraining, no manual tuning—adaptive learning built-in
Ship better search in 5 minutes
Full code example with 5-minute integration. Upload documents, search naturally, get smarter automatically.
No index building or embeddings to generate. Start searching immediately.
See the exploration tree, not black box scores. Understand why each result was chosen.
Use any language, no special SDKs required. Simple HTTP endpoints.
Watch your RAG get smarter. Track performance improvements over time.
Real-time notifications for search events and learning milestones.
From zero to production-ready search in minutes, not hours.
Built for documents that matter
Find exact clauses, not "similar sounding" text. Learns your contract patterns.
Navigate complex technical docs with context awareness. Adapts to developer queries.
Citation-accurate retrieval that respects academic structure. No hallucinated references.
User guides and product manuals that understand context. Learns from user questions over time.
Organize and search project documents, notes, and assets. Adapts to your workflow patterns.
Internal wikis, policies, and procedures. Adapts to your company's unique language.
Knowledge bases and FAQs that learn from ticket patterns. Reduces support volume by 42%.
Simple, usage-based pricing
Pay only for what you use. No subscriptions. No hidden fees.
Start exploring with generous free usage every month
One-time processing cost. Searchable forever.
Includes multi-document search & adaptive learning.
Pay only for what you use. No subscriptions. No commitments.
• 1,000 documents
• 100K queries
Save 15%
Pre-purchase credits at a discount for predictable workloads
Custom
• Volume discounts (30-50% off)
• Priority support & SLA
• Custom integrations
• On-premise options
Volume discounts, dedicated support, and custom solutions
Why usage-based pricing?
No waste
Only pay for documents you actually process and queries you run
Predictable
Clear per-unit pricing. No surprise vector DB bills
Scale freely
Start small, grow big. No tier migrations needed
All processing includes hierarchical embeddings, adaptive learning, and Monte Carlo search. No extra fees.