Amazon Finance builds an AI assistant using Amazon Bedrock and Amazon Kendra for data discovery and business insights
Finance analysts at Amazon Finance spent significant time manually browsing data catalogs and reconciling data from disparate sources, with institutional knowledge siloed across documents and legacy systems, and traditional keyword-based searches failing to surface contextually relevant financial information.
Traditional keyword-based search tools fell short: the previous manual search process had an estimated success rate of only 35% and required multiple iterations, while traditional keyword-based search achieved only 45–50% precision.
The AI assistant reduced search time by 30% and improved search result accuracy by 80%.
Average time to find relevant information fell from 45–60 minutes to 5–10 minutes—an 85% efficiency improvement—and 92% of analysts prefer the new system over traditional search methods.
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Frequently asked questions
What did this team achieve with this AI workflow?
The AI assistant reduced search time by 30% and improved search result accuracy by 80%.
What tools did this team use?
Amazon Bedrock, Amazon Kendra, Anthropic's Claude 3 Sonnet, Streamlit, RAG.
What results were reported?
Search time reduction: 30%; Search result accuracy improvement: 80%; Data discovery precision (new system, no metadata enrichment): 65%; Data discovery recall (new system, no metadata enrichment): 60% (source-reported, not independently verified).
What failed first in this deployment?
Traditional keyword-based search tools fell short: the previous manual search process had an estimated success rate of only 35% and required multiple iterations, while traditional keyword-based search achieved only 45…
How is this finance ops AI workflow structured?
User submits natural language query → Kendra semantic document retrieval → RAG context passed to LLM → Claude generates response → Response displayed with history → Feedback stored in S3.