Your retrieval is only as good as what you indexed. tinyAI Labs models read, classify, extract and redact millions of pages on plain CPUs before anything reaches a vector store or an agent. Same document in, same chunks out, every time.
Layout, tables, stamps, scan quality. Pages become regions, not pixel soup.
Classify each document, extract typed fields, chunk by structure instead of by token count.
Resolve entities, dedupe versions, link documents to the customer and case they belong to.
Redact PII, check quality, watch for drift in incoming data before it lands.
Clean, tagged, attributed chunks into your vector store, graph or agent memory.
Rebuild the index next quarter and get identical chunks and metadata. When an answer changes, you know it was the data, not the parser.
Using an LLM to pre-read every page is the most expensive way to build a knowledge base. CPU models make ingestion a fixed cost, not a meter.
Redaction happens inside your perimeter, before embedding. Personal data never reaches a hosted model, an embedding API or a log.
Each chunk and field carries the page, line and model version it came from. Your agent can cite its source and your auditor can check it.
Point it at a bucket or a share. Run it as a container, a CLI or a step in your existing ETL. Scale out by adding cores, not GPUs.
We'll run them through the pipeline inside your environment and hand back the index, the lineage and the bill. Compare it with what you pay today.