Solutions · RAG ingestion and PII safety
Knowledge bases from your documents, without the PII
Retrieval is only as safe as what goes into it. FuseAIs extracts your documents, tokenizes personal data before a single chunk is embedded, and keeps the originals restorable inside your stack — so public models never see the PII.
Why it's hard today
Once a name, an SSN or a salary is embedded, it lives in the vector index, in every chunk that retrieves it, and in the logs of every model that reads those chunks. Removing it later means finding every copy. Most ingestion pipelines never look for personal data at all — they chunk whatever the document contained.
How it works on FuseAIs
The same pipeline runs whichever model you route to, with PII tokenized before public models see it.
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Bring the documents
PDFs, scans, Office files, email and exports from your systems.
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Extract the text
Text layers are read directly; scanned pages go through OCR, with tables kept as tables.
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Tokenize PII before chunking
Names, contact details, IDs and card numbers become reversible tokens. The token map stays with you.
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Chunk, embed and index
Duplicate and near-duplicate documents are dropped first, and only tokenized text is embedded, so the knowledge base holds no personal data to leak.
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Answer and restore
Answers come back with tokens; your application restores the real values for the people allowed to see them.
What you get
- A knowledge base that is safe to point any approved model at
- No personal data in embeddings, retrieved chunks or model logs
- Retention and deletion handled on the originals, not hunted across an index
- The same pipeline for new documents as they arrive
Planning a knowledge base on sensitive documents?
Tell us what goes in and who asks the questions, and we will show you the ingestion pipeline on your own files.