RAGproof.io — vector DB audit

Your RAG index is quietly going stale, orphaned, and leaking data.

RAGproof audits your vector database read-only — pgvector, Qdrant, Weaviate, Chroma, or Pinecone — and reports exactly which content is stale, orphaned, duplicated, or deleted-but-still-retrievable. Nothing leaves your environment.

Used only to respond to this request.

audit_report.txt
// index health — acme-prod-index
31% of vectors older than source document
4,812 orphaned embeddings (no source record)
1,203 duplicate clusters, avg. 3.4 copies
12 deleted documents still retrievable
→ GDPR Art. 17 erasure exposure

01 — What you get

One report. Every stale, orphaned, and legally exposed vector, named.

  • 01

    Freshness map

    Every vector plotted against its source document's last-modified date — surfaces drift at a glance.

  • 02

    Orphan list

    Embeddings with no matching source record left in your system of record.

  • 03

    Duplicate clusters

    Near-identical chunks re-embedded across ingestion runs, grouped and counted.

  • 04

    Ghost-vector / erasure check

    Deleted-but-still-retrievable content — flagged against GDPR Article 17.

  • 05

    Prioritized fix plan

    Ranked remediation steps, ordered by retrieval-quality and compliance risk.

02 — How it works

Three steps. Nothing installed permanently, nothing exported.

  1. 01

    Read-only connection

    You grant read-only credentials scoped to the index. We never write, delete, or modify.

  2. 02

    Self-hosted scan, your VPC

    The scan runs inside your environment. No vectors or source content leave your infrastructure.

  3. 03

    Report + walkthrough call

    One-page report, delivered and walked through live — findings, risk, and fix order.

03 — Why now

Why now

Staleness is invisible — until users stop trusting the answers.

There's no error, no alert, no failed request. Retrieval keeps returning results — they're just increasingly wrong. By the time users notice, trust in the system is already gone.

Deleted-but-retrievable content is a data leak — and a GDPR Article 17 problem.

If a document is erased from your source of record but its embedding still surfaces in retrieval, that's not just a compliance gap — it's a live data leak. The right to erasure hasn't actually been honored, and every query against that index re-exposes content you were supposed to have deleted.

04 — FAQ

Stale, orphaned, duplicate, and leaked vectors — the questions we get most.

What counts as a "stale" vector?

A vector goes stale the moment its source document changes and the embedding doesn't. A policy gets revised, a price changes, a support article gets rewritten — but the old embedding is still sitting in your RAG index, still being retrieved, still being handed to the model as ground truth. RAGproof compares every vector's embedding timestamp against its source document's last-modified date and flags exactly which ones have drifted.

What's the difference between a stale vector and an orphaned one?

Stale means the source document still exists but has changed since it was embedded. Orphaned means the source record is gone entirely — deleted, moved, or renamed — and the embedding is left behind with nothing to reconcile against. Both produce confidently wrong answers in retrieval; they just fail for different reasons, which is why RAGproof reports them as separate categories rather than one vague "drift" score.

Why do duplicate clusters hurt retrieval quality?

Every re-run of an ingestion pipeline that doesn't dedupe against what's already indexed adds another near-identical copy of the same chunk. Once you have a few thousand of these duplicate clusters, they crowd your top-k results with redundant near-duplicates instead of genuinely relevant context, quietly degrading answer quality without throwing a single error. RAGproof groups duplicate clusters so you can collapse them with confidence instead of guessing.

Is deleted-but-retrievable content really a data leak?

Yes. If a document is deleted from your system of record under a GDPR Article 17 erasure request, but its embedding still surfaces in retrieval, that content hasn't actually been erased — it's leaking out through every query that touches the index. We cover a real 13-month case, including how a single deleted document kept resurfacing in production, in our RAG index decay teardown.

Which vector databases does RAGproof support?

pgvector, Qdrant, Weaviate, Chroma, and Pinecone today, connected with read-only credentials scoped to a single index so we can never write, delete, or modify anything. If you're running something else, mention it on the discovery call — most audits generalize once we can query the index and its metadata, regardless of which vector database is underneath.

Does anything leave our VPC during the audit?

No. The scan itself is self-hosted and runs inside your environment end to end; RAGproof never exports raw vectors, embeddings, or source content anywhere. Only the finished report — freshness map, orphan list, duplicate clusters, and the erasure/leak check — leaves your infrastructure. Full details on what we do and don't collect are in our privacy policy.