Blog/
From OCR to vector database in one pipeline
Upload a document, run layout OCR + QC, chunk with structure, and push embeddings to Pinecone, Qdrant, Weaviate, or Postgres.
Teams often glue three vendors together: OCR here, chunking there, embeddings somewhere else, then a brittle script into Pinecone. OCRs.ai collapses that into one job lifecycle — extract, QC, rebuild layout, embed, push.
Structure-aware chunks
Chunks follow headings, paragraphs, and tables instead of fixed character windows. That keeps a table row together and avoids cutting a section title away from its body.
Each chunk can carry bbox and page metadata so your RAG UI can cite the exact region on the source PDF.
Destinations
Push to Pinecone, Qdrant, Weaviate, or your own Postgres with pgvector — or use the zero-config store built into the product while you prototype.
The same REST API that uploads a file can poll status and read completed pages, so agents and backends stay in one auth surface.
Try layout OCR with AI QC on OCRs.ai
Free credits at signup — then metered pages and video.