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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits natural language query
The user asks a question in a chat box after authentication.
Tools used
Amazon BedrockAmazon KendraAnthropic's Claude 3 SonnetStreamlitRAG
Outcome
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.
What failed first
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.