🚀 Research PreviewWe’re currently in research preview! We’re excited to share our system with you, and we would love to hear your feedback.Enterprise Users: Need custom APIs tailored to your specific use case? Submit a request through our form →
Who We Are
Vedaya is a platform for building intelligent knowledge systems powered by advanced knowledge graph technology and retrieval-augmented generation (RAG). We transform your unstructured data into a queryable, interconnected knowledge graph that understands context, relationships, and meaning.What is Vedaya?
Vedaya is an enterprise-grade knowledge infrastructure platform that solves the fundamental scaling problem of traditional RAG systems. While standard vector-based RAG degrades significantly beyond 10,000 documents and fails completely at 100,000+, Vedaya maintains 93% accuracy even at millions of documents through our proprietary unified knowledge framework.The Scale Problem We Solve
Traditional RAG systems face critical failures at enterprise scale:- Accuracy degradation: Performance drops 48% beyond 10k pages
- Lost relationships: Vector embeddings can’t preserve structural connections
- Combinatorial complexity: Graph traversals become exponentially slow
- Integration overhead: Separate systems for vectors and graphs
Core Innovation
Our proprietary technology creates a unified mathematical framework that:- Preserves Relationships: Maintains both semantic meaning and structural connections in a single compressed representation
- 4× Compression: Reduces storage and memory requirements while improving accuracy
- Sub-second Latency: Achieves vector-speed retrieval with graph-level reasoning (120ms median at 50M documents)
- Continuous Learning: Improves with every interaction through adaptive algorithms
Proven Performance
82% vs 47%
Accuracy on SEC filing generation compared to standard RAG
93% at Scale
Maintained accuracy at 10M+ documents where others fail
98.7% Recall
PII detection accuracy in production deployments
When to Use Vedaya
Perfect For
- Enterprise knowledge bases (10,000-10M+ documents)
- Regulated industries (legal, medical, financial)
- Complex multi-hop reasoning and relationship queries
- Production systems needing consistent sub-second latency
- Applications where relationships between concepts matter
- Systems requiring both precision and recall
Consider Alternatives For
- Simple FAQ bots with < 1,000 documents
- Pure keyword search without semantic needs
- One-time analysis without ongoing updates
- Applications where relationships don’t matter
Performance at Scale
| Scale | Vedaya Accuracy | Traditional RAG | GraphRAG | Vedaya Latency |
|---|---|---|---|---|
| 1K docs | 95.2% | 92.1% | 90.3% | < 50ms |
| 10K docs | 94.8% | 76.3% | 85.2% | < 80ms |
| 100K docs | 93.9% | 48.2% | 71.4% | < 120ms |
| 1M docs | 93.2% | 24.2% | Failed* | < 150ms |
| 10M docs | 92.8% | Failed | Failed* | < 200ms |
🎥 Product Demo
How to Use Vedaya
Get started with Vedaya through multiple integration paths:Playground
Experiment with our API in an interactive environment - test queries, upload documents, and explore your knowledge graph visually
OpenAI Compatible
Drop-in replacement for OpenAI API with enhanced RAG capabilities using special
vedaya-* modelsNative API
Full control with our native API for advanced knowledge graph operations and customization
Core Capabilities
Document Processing
Upload PDFs, DOCX, TXT, or raw text. Automatic extraction and processing in seconds, not minutes.
Knowledge Graph
Automatic entity and relationship extraction. Traverse connections and understand context.
Smart Retrieval
Four RAG modes: naive, local (entity-focused), global (relationship-focused), and hybrid.
Multi-Turn Conversations
Maintain context across conversations. Build intelligent chatbots and Q&A systems.
Ready to Build?
Vedaya is already powering intelligent applications across research, education, and enterprise knowledge management.Quick Start
Upload your first document and query it in under 2 minutes - no authentication required
Interactive Playground
Test all API endpoints directly in your browser with our comprehensive playground
Developer Resources
API Reference
Complete API documentation with interactive examples
Integration Guides
Step-by-step guides for common integration patterns
Best Practices
Architecture patterns and optimization techniques
Technical Advantages
| Capability | Vedaya | Vector RAG | GraphRAG |
|---|---|---|---|
| Relationship Preservation | ✓ In compressed embeddings | ✗ Lost during encoding | ✓ Runtime traversal required |
| Latency Scaling | O(log n) | O(log n) + reranking | O(b^d) exponential |
| Memory Footprint | 4× smaller | Proportional to corpus | Proportional to edges + corpus |
| Infrastructure | Standard vector DB | Vector DB | Graph DB + Vector DB |
| Accuracy at 1M docs | 93.2% | 24.2% | System failure |
| Multi-hop Reasoning | Native support | Not possible | Slow traversals |
| Incremental Updates | Real-time | Full reindexing | Complex synchronization |