The Problem
Most AI platforms rely solely on vector embeddings for retrieval. That works for vague, conceptual queries. But when your agent needs to find invoice #INV-2024-3847, a specific error code, or a contract clause with exact dollar amounts, semantic similarity falls apart.
The result? Your agent confidently returns the wrong document, hallucinates details, or misses the answer entirely. That's why enterprise teams can't trust vector-only RAG in production.
Returns 5 invoices from 2024 that are "semantically similar." None of them are #3847. The agent picks one and presents wrong data with full confidence.
Full-text search matches the exact invoice number. Vector search confirms semantic relevance. The agent gets the right document, every time.
How It Works
Every query runs through both pipelines simultaneously. Results are scored, merged, and ranked so your agents always get the most relevant, accurate answer.
Understands meaning, not just keywords. "Q4 revenue projections" finds documents about "fourth quarter financial forecasts" even if those exact words don't appear.
Exact matching when precision matters. Invoice numbers, error codes, product SKUs, legal clause references. No approximation, no guessing.
Semantic
Meaning-based ranking
Reciprocal Rank Fusion
Keyword
Exact-match ranking
Document Ingestion
Drop files in and they're indexed. Intelligent parsing extracts clean text from any format while preserving structure, tables, and meaning.
Tables, headers, lists, and metadata are all preserved. Scanned documents handled with OCR automatically.
Millions of documents indexed and searchable instantly. Enterprise-scale performance without infrastructure headaches.
Add or update documents without re-indexing everything. Your knowledge base stays current as your data changes.
Search results feed directly into your UraiJS tools. Agents combine knowledge with live integrations for grounded answers.
Security
Fine-grained access control ensures agents and users only see what they're authorized to see. Permissions are tied to your identity provider.
Control who can access which documents. Sales sees sales docs. Engineering sees engineering docs. Agents respect the same boundaries.
Multi-tenant by default. Each team or client has their own knowledge base, their own permissions, their own search index.
Every query, every document access is logged. Know exactly what your agents are reading and what they're using to generate answers.