The Enterprise RAG Challenge
Traditional RAG systems face fundamental limitations at enterprise scale that cannot be solved by simply adding more compute or better models.The Curse of Dimensionality
When vector databases grow beyond 10,000 documents, high-dimensional embeddings begin to overlap. This creates a cascade of problems:- Semantic Blurring: Distinct concepts become indistinguishable in vector space
- Lost Context: Relationships between entities disappear completely
- Retrieval Degradation: Accuracy drops from 92% to 24% at 1M documents
- Exponential Complexity: Graph traversals become computationally intractable
Vedaya’s Breakthrough
Our proprietary unified knowledge framework solves these problems through a fundamentally different approach that preserves both semantic meaning and structural relationships in a single mathematical representation.Core Innovation
Unified Representation
Unlike systems that separate vectors and graphs, we maintain both semantic and relational information in a single compressed structure
4× Compression
Our approach reduces storage requirements while actually improving accuracy through latent pattern discovery
Adaptive Intelligence
The system automatically optimizes its internal representations based on your data patterns and usage
Incremental Learning
New documents integrate seamlessly without full reindexing, maintaining performance at scale
Proven Results
Academic Benchmarks
Our technology achieves state-of-the-art performance on standard knowledge completion benchmarks: FB15K-237 Dataset (Knowledge Graph Completion)- Hits@1: 0.54 (vs. 0.32 best baseline) - 69% improvement
- Hits@10: 0.76 (vs. 0.52 best baseline) - 46% improvement
- Training Speed: 1.7× faster than neural baselines
- 1M documents: 35.8% accuracy (vs. 24.2% for Vector RAG)
- 10M documents: 93.2% retention (vs. complete failure for alternatives)
- Query latency: 4.3ms (practical for production use)
Real-World Deployments
Fyler: SEC Filing Generation
Fyler: SEC Filing Generation
Challenge: Generate accurate SEC Form 8-K filings requiring precise regulatory complianceResults:
- 82% pass rate (vs. 47% for standard RAG)
- 35-point improvement in compliance accuracy
- Zero critical errors in executive transition filings
Aurva: PII Detection Enhancement
Aurva: PII Detection Enhancement
Challenge: Detect personally identifiable information across mixed finance and healthcare tablesResults:
- Recall improved from 81% to 98.7%
- Less than 15ms overhead per 1k rows
- Edge-deployable with compressed models
How It Works (High Level)
1. Intelligent Extraction
Our domain-specific models understand your data:- Legal: Contracts, regulations, case law
- Financial: Filings, reports, disclosures
- Healthcare: Clinical notes, research papers
- Technical: Code, documentation, specs
2. Unified Processing
Documents are transformed into our proprietary knowledge representation:- Entities and relationships extracted automatically
- Semantic meaning preserved alongside structural connections
- Compression applied while maintaining accuracy
- Compatible with existing vector database infrastructure
3. Adaptive Retrieval
Queries leverage the full power of unified knowledge:- Semantic Search: Find conceptually similar information
- Relationship Traversal: Follow connections between entities
- Multi-hop Reasoning: Answer complex questions requiring inference
- Hybrid Queries: Combine semantic and structural constraints
Key Differentiators
| Feature | Vedaya | Traditional Approach | Business Impact |
|---|---|---|---|
| Adaptive Processing | Automatically optimizes based on data patterns | Fixed algorithms regardless of content | Higher accuracy with less tuning |
| Incremental Updates | Add documents without reprocessing | Full reindexing required | Real-time knowledge expansion |
| Predictive Caching | 90% cache hit rate through usage learning | 40% with standard LRU | 40-60ms latency vs 200ms |
| Dual-Index Architecture | Entity and concept keys for precision | Single embedding space | Find exact entities or themes |
Domain Specialization
Our technology includes pre-trained models for specific industries:Legal & Compliance
- Contract analysis
- Regulatory compliance
- Case law research
- Risk assessment
Financial Services
- SEC filing analysis
- Risk reporting
- Transaction monitoring
- Audit support
Healthcare & Life Sciences
- Clinical documentation
- Research synthesis
- Protocol adherence
- Safety monitoring
Technology & Engineering
- Code understanding
- Documentation search
- Architecture analysis
- Security review
Infrastructure Benefits
Compatibility
- Works with existing vector databases: Pinecone, Weaviate, pgvector
- No graph database required: Eliminates complex dual-system management
- Standard REST API: Easy integration with any tech stack
- OpenAI compatible: Drop-in replacement for existing workflows
Efficiency
- 4× memory reduction: Store more knowledge in less space
- 47% cost reduction: Lower infrastructure spend at scale
- O(log n) scaling: Consistent performance regardless of size
- Edge deployable: Compressed models for distributed systems
Performance Guarantees
At 50 million documents with 200 queries per second:
- Median latency: 120ms
- P99 latency: 300ms
- Accuracy: >92%
- Uptime: 99.9%
Get Started
Ready to experience the difference? Our technology is available through:Cloud API
Start querying in under 2 minutes with our hosted service
On-Premise
Deploy within your infrastructure for complete control
Hybrid
Best of both worlds with flexible deployment options