A system that ingests thousands of documents—legal contracts, medical records, compliance files—and makes them searchable by meaning. Users ask questions in plain language; the system finds relevant passages across the entire corpus.
Not a simple search box. A complete document intelligence platform.
Pure vector search finds semantically similar content but misses exact terminology. Pure keyword search finds exact matches but misses paraphrases. HeroVox combines both.
Before users even search, the system groups similar documents automatically. Upload 10,000 contracts and it finds natural clusters—employment, vendor, NDAs, licensing.
Documents may be in English, Spanish, or mixed. The system detects language per-document and per-paragraph, routes to appropriate models.
Real documents aren't clean text files. Scanned images need OCR. Multi-column layouts need structure detection. Tables need special handling.
Vector embeddings can drift over time as models update or data distributions change. The system tracks embedding quality metrics and alerts when re-indexing is needed.
Runs on a 12-container Docker stack with proper separation—API servers, embedding workers, search indices, caching layers. HAProxy handles load balancing and SSL.
From raw PDF to searchable knowledge in 5 stages.
PDF, Word, Scans
OCR + Structure
Semantic splitting
Vector encoding
Pinecone/ONNX
How organizations use HeroVox to unlock their document archives.
Law firm has 50,000 contracts accumulated over 20 years. Finding relevant precedents used to take paralegals days.
Hospital network needs to find all patients with specific condition combinations for clinical trials. Records spread across 15 years of mixed formats.
Company preparing for SOX audit needs to find all control documentation across SharePoint, email archives, and policy documents.
Enterprise-grade infrastructure for document intelligence.