docimprint

Why verifiable document memory beats RAG alone

What problem does RAG leave unsolved?

RAG answers 'what text is similar to this query?' It cannot prove the retrieved passage matches an authoritative document at capture time. In compliance, legal, and finance workflows, similarity is not enough — you need cryptographic proof of provenance.

How does DocImprint complement RAG pipelines?

Capture documents with POST /v1/extract to produce evidence bundles, verify them offline, then feed verified chunks into your vector store. Agents cite bundle IDs and Merkle proofs instead of unverifiable embedding matches.

Which should autonomous agents use by default?

Use DocImprint when decisions require auditable citations — contracts, filings, invoices, policies. Use RAG for exploratory search over already-verified corpora. High-stakes agents should capture proof first, then retrieve.

Comparison at a glance

CapabilityTypical RAGDocImprint
Semantic searchYesVia Collections
Tamper-evident captureNoYes (evidence bundles)
Offline verificationNoFree verify endpoint
Chain-of-custodyNoLegal hold + notarize

FAQ

Can RAG replace verifiable document memory?

RAG retrieves similar text chunks from a vector store. It does not prove the text was captured at a specific time from a specific document. DocImprint evidence bundles bind extracted bytes to cryptographic hashes and optional on-chain anchors.

When should I use RAG with DocImprint?

Use RAG for broad semantic search across large corpora. Use DocImprint when you need tamper-evident capture, offline verification, legal chain-of-custody, or agent citations that auditors can validate without trusting your server.

Does DocImprint store embeddings?

DocImprint Collections can index bundle content for semantic search, but the source of truth is the signed evidence bundle — not an embedding alone. You can combine both: capture with DocImprint, then embed verified chunks in your vector store.

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