Overview
ColiVara is a platform that builds visual embeddings for documents, enabling high-quality retrieval for both images and text without relying on OCR. By preserving visual layout and content, it supports semantic search over scanned documents, PDFs with figures and tables, and other image-rich materials.
Key Features
- Visual-embedding centric: generates embeddings directly from visual documents to improve image and complex-document retrieval.
- Unified multimodal search: indexes images and text together to simplify pipelines and improve recall.
- Efficient storage and retrieval: optimized indexing and query flows for balanced accuracy and throughput.
- Reduced preprocessing: avoids brittle OCR/text-extraction steps that lose layout or figures.
Use Cases
- Document management: semantic search across scanned reports, manuals, and papers with images and tables.
- Media & publishing: search and recommendation while preserving layout and imagery.
- Legal & compliance: precise retrieval over evidence containing images and tables.
- Enterprise knowledge bases: RAG-enabled search across mixed image/text knowledge assets.
Technical Details
- Visual-model-based embedding generation with support for multiple vision encoders and vector backends.
- Adaptable to vector databases and local index solutions for flexible deployment.
- Retrieval and reranking strategies tuned for multimodal similarity metrics.
- Provides APIs and deployment tooling for integration and production readiness.