Amazon’s Finance Technology teams have introduced a generative AI system on AWS that dramatically simplifies the processing of regulatory inquiries from global authorities. The solution addresses longstanding inefficiencies in compliance operations by automating document analysis, information synthesis, and response drafting while preserving strict regulatory standards.
Regulatory bodies worldwide issue requests that vary widely in format, scope, and urgency.
Finance teams must comb through thousands of historical records—spanning PDFs, presentations, spreadsheets, and more—to locate precedents, extract relevant data, and assemble accurate replies within tight deadlines.
As Amazon’s operations expanded, these demands created bottlenecks: scattered knowledge across disparate systems, the need to sustain context across lengthy back-and-forth discussions, and the challenge of monitoring AI outputs for reliability and compliance.
The new platform tackles these issues through a retrieval-augmented generation (RAG) architecture built on Amazon Bedrock.
Each finance team maintains its own secure knowledge base, populated with proprietary documents and reference materials.
Amazon OpenSearch Serverless serves as the vector database, enabling rapid semantic searches across massive document collections.
The system processes inquiries via real-time chat interfaces powered by the Converse Stream API and Claude Sonnet 4.5, ensuring responses remain grounded in verified sources.
Document ingestion follows a fully automated, serverless pipeline.
Users upload files through a client application, triggering AWS Lambda functions that generate pre-signed Amazon S3 URLs for secure transfers.
Once stored, additional Lambda processes handle format conversion and initiate ingestion into Bedrock Knowledge Bases.
Multimodal content such as charts and tables requires no manual preparation, as Bedrock Data Automation extracts it automatically.
Documents are segmented using hierarchical chunking—preserving parent-child relationships between sections—and embedded with Amazon Titan Text Embeddings for precise retrieval.
This approach balances granularity with sufficient context, allowing the system to scale efficiently as inquiry volumes grow year over year.
The conversational interface delivers a natural, multi-turn experience. WebSocket connections via Amazon API Gateway enable streaming responses, so users see answers as they generate.
Incoming queries are sanitized against prompt-injection risks, then classified by intent.
For knowledge-intensive questions, a lighter model (Claude 3.5 Haiku) expands the original phrasing into multiple variations to capture acronyms and shorthand common in finance.
Parallel vector searches retrieve the most relevant chunks, which are combined with full conversation history stored in Amazon DynamoDB. The assembled context feeds the primary model to produce coherent, citation-backed replies.
This workflow reduces retrieval latency from seconds to under two seconds through multi-threading, supporting fluid iterative discussions.
Comprehensive observability underpins the entire solution. OpenTelemetry instrumentation integrated with a self-hosted Langfuse instance captures end-to-end traces, including latency, token usage, prompt details, and model decisions.
Teams can trace data lineage, identify hallucinations, flag outdated references, and continuously refine prompts and retrieval strategies—all while maintaining vendor-neutral flexibility for future monitoring needs.
By eliminating manual drudgery and enforcing traceable, governed AI practices, the platform accelerates response times, enhances accuracy, and provides immutable audit trails through DynamoDB logs.
Its serverless design delivers automatic scaling and enterprise-grade security without operational overhead. Amazon Finance now handles complex regulatory dialogues with greater speed and confidence, setting a practical blueprint for other organizations facing knowledge-intensive compliance challenges.