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RAG retrieval audit · zero setup
Find why your RAG retrieves the wrong context.
Test the fixes on your own corpus before production. RetrievalGate diagnoses retrieval failures, compares realistic configurations and recommends what your team should actually change.
No SDKNo production accessNo vector DB credentials
Recommendation
Hybrid retrieval
Current74.2%
Candidate91.8%
Current retrieval74.2%
Recommended91.8%
Primary issueExact identifier retrieval
Best tested fixDense to Hybrid
+17.6 pts Recall@5Test the improvement before you change production.
A release gate for retrieval
Measure the retrieval failures before they become product failures.
Give us a corpus and, when available, real user queries with their relevant source. We measure whether the right context is found.
RetrievalGate then tests the fixes that matter: chunking, embeddings, dense, BM25, hybrid retrieval and reranking. You see the gains, regressions and a recommendation grounded in the run.
See how a retrieval audit works →One bounded workflow
From corpus to a decision your team can implement.
The benchmark mechanics stay behind the result. You get the evidence needed to choose, implement and validate.
Add real queries when you have them
An optional evaluation CSV makes the benchmark reflect the questions and source documents that matter to your users.
Run the free snapshot
Get corpus health, a baseline comparison, likely failure modes and the evidence quality of the result.
Unlock only when the signal is useful
Full Audit tests the broader matrix, exposes regressions and gives you the configuration and validation rerun to use before release.
The artifact that matters
A decision your team can implement.
Not another observability dashboard. Every full audit ends with a compact configuration, expected impact, known regressions and an explicit verdict.
- Recommended chunking and retrieval settings
- Segment-level evidence and failed-query examples
- Migration and re-indexing cost considerations
- One validation rerun included within 14 days
Implementation cardv1.0
ChunkingHeading-aware · 700 / 80
Embeddingvoyage-4
RetrievalHybrid BM25 + Dense
RerankerEnabled · final top 5
DecisionGO WITH CONDITIONS
Built around the decision you face
One audit. Three ways to use it.
Agency / consultant
Turn architecture choices into a client-ready deliverable.
Replace screenshots and intuition with evidence your client can act on.
Explore agency audits →RAG in productionTest retrieval changes before you re-index or ship.
Find failure modes, compare fixes and catch critical regressions.
Audit an existing RAG →New RAG systemChoose a retrieval architecture on your own corpus.
Benchmark realistic options before infrastructure decisions harden.
Benchmark a new RAG →No invented certainty
Every recommendation states how strong the evidence is.
Labelled production evidence
Real queries paired with known relevant documents.
Real queries, inferred relevance
Your query distribution with synthetic reference construction.
Synthetic benchmark
Useful for orientation, explicitly reported with its limitations.
Founding offer
Start with the evidence. Pay for the production decision.
Start with a free snapshot. Upgrade when the evidence shows a full comparison can answer your retrieval decision.
Full Retrieval Audit€99one-time audit · one validation rerun includedCompare the audits
Straight answers
What RetrievalGate audits — and what it does not.
Why not use Ragas, DeepEval or an eval platform?
Those tools help you build and operate evaluation workflows. RetrievalGate performs a bounded retrieval benchmark and returns the interpreted production decision.
Do you need access to our production system?
No. The audit works from the corpus and optional query evidence you provide. It does not require vector database credentials or an SDK.
Does this evaluate hallucinations and final answers?
No. RetrievalGate isolates the retrieval layer. It does not claim to evaluate your complete AI application or guarantee final LLM answer quality.
What if the data cannot support a meaningful decision?
The verdict is insufficient evidence. RetrievalGate reports the limitation instead of manufacturing a recommendation.
Make the retrieval decision
Stop guessing. Test the fixes before you ship.
Start with a free snapshot on your own corpus. No SDK, production access or vector database credentials required.