Production RAG Starter Lab
Production RAG Fraud Investigation Lab
See how a production RAG pipeline retrieves, filters and validates evidence before recommending a decision.
AI gathers & verifies evidence. You decide.
The assistant retrieves, filters and evaluates relevant evidence. You review the cited brief and make the final decision.
Scroll horizontally to follow the evidence →
Evidence sources
RAG evidence pipeline
You decide
AI does not approve or block the payment.
Inside the pipeline · Retrieve
The assistant searches for evidence, not a final answer.
The query combines the transaction, customer, device, merchant and policy context. Retrieval produces candidate evidence with a relevance score before any text reaches the answer model.
- 98% · Transaction event FR-2048
- 94% · Customer behavior profile
- 91% · Device and session intelligence
- 87% · Active high-value transaction policy
Build it yourself
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- Inspectable retrieval pipeline
- Metadata and access filters
- Grounded answers with citations
- Evaluation examples and failure cases
Production principle
Retrieval quality is a decision boundary.
A plausible answer is not enough. A production RAG system must use current, permitted evidence—and make uncertainty visible before a person acts on it.