For new retrieval systems

Choose your RAG architecture on your data, not a leaderboard.

Stop guessing chunk size, embeddings and retrieval strategy. RetrievalGate benchmarks a practical set of options on your corpus before infrastructure decisions become expensive to reverse.

Benchmark a new RAG I already have a RAG

A practical starting point

Move from corpus to an evidence-backed baseline.

Inspect the corpus

Surface parsing problems, duplication, document structure and suspicious source material.

Build query coverage

Prioritize real queries when provided and fill important gaps with synthetic evaluation queries.

Select the baseline

Compare realistic retrieval configurations and return the strongest defensible starting point.

What gets compared

The choices that shape retrieval quality.

Embedding candidate selection

Do not choose a vector model from a public leaderboard alone.

A model can improve broad semantic queries while weakening the identifiers, numbers or languages that matter to your users. Hold the evidence constant and inspect the trade-off before re-indexing.

Learn how to benchmark embedding models

A bounded benchmark by design

“Enough evidence to choose a materially better configuration.”

RetrievalGate does not promise a mathematically optimal pipeline. It narrows the realistic choices, makes trade-offs visible and gives your team a baseline it can explain.

Build with evidence

Start your retrieval architecture with fewer assumptions.

Upload a corpus and get a free retrieval snapshot before committing to your first indexing strategy.

Run a free retrieval audit Benchmark a new RAG