Normalized claim
Retrieval latency: 80% decrease
“This optimization reduced retrieval latency from 10 seconds (sequential processing) to under 2 seconds”
Amazon FinTech regulatory inquiry automation with Amazon Bedrock Knowledge Bases
Use case typeRegulatory inquiry automationUpdated May 12, 2026
Amazon Finance Technology built a scalable AI application for regulatory inquiries across jurisdictions using Amazon Bedrock Knowledge Bases and AWS serverless services. The solution uses dedicated knowledge bases per team, retrieval-augmented generation, streaming conversations, guardrails, DynamoDB conversation state, and OpenTelemetry/Langfuse observability.
- Organization
- Amazon Finance Technology
- Industry
- Finance
- Location
- United States
- Published
- May 2026
Reported outcomes
−80%
retrieval latencyTime & speed
Strategic outcomes
Risk & complianceEnabled secure, regulatory-compliant inquiry handlingRisk & complianceCreated an immutable audit trail for compliance reviewSpeed & agilityDelivered real-time streaming responses
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Amazon Finance Technology
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Machine Learning Blog
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 2 of 2
- 1Regulatory inquiry automation
- 2Workflow automation
- Regulatory inquiries required reviewing thousands of historical documents across many formats and jurisdictions.
- Teams needed multi-turn conversational context, auditability, and protection against hallucinations and outdated guidance.
- Built an intelligent regulatory response automation system with Amazon Bedrock Knowledge Bases and OpenSearch Serverless for vector retrieval.
- Used Converse Stream API for low-latency grounded responses, DynamoDB for conversation history, Cognito for authentication, Bedrock Guardrails for sensitive data filtering, and OTEL plus Langfuse for full tracing.
- Implemented parallel query expansion and retrieval to improve response latency and maintain compliance.
- Reduced retrieval latency from about 10 seconds sequential to under 2 seconds.
- Enabled secure, context-aware, regulatory-compliant multi-turn assistance with streaming responses and immutable audit trails.
Architecture
Serverless AWS architecture using Amazon Bedrock Knowledge Bases, Amazon OpenSearch Serverless, Amazon Bedrock Converse Stream API, Amazon Bedrock Guardrails, Amazon Bedrock Data Automation, Amazon API Gateway WebSockets, AWS Lambda, Amazon DynamoDB, Amazon Cognito, OpenTelemetry, and self-hosted Langfuse. The solution uses knowledge-base-per-team ingestion, query expansion, parallel retrieval, streaming generation, and persistently stored conversation history and traces.
Sources & evidence1
Evidence: Low35/100Evidence strength
- Customer explicitly identified
- Quantified outcome available
- Technical implementation details available
Type: Blog PostPublished: May 12, 2026Publisher: AWSEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
AI patterns
Browse the catalog
Was this useful?
Community
Comments
Loading comments...