Build a Financial Research Assistant with Cohere AI on AWS
Summary
- A reference architecture for a financial research assistant on AWS combines Cohere Embed, Rerank, and Parse with Amazon SageMaker AI, OpenSearch Serverless, and Amazon Bedrock.
- The system optimizes retrieval quality and latency by using Embed Pro for document indexing and Embed Fast for queries, supported by configurable vector quantization.
- Cohere Parse handles scanned and table-heavy financial documents to preserve data relationships, while Anthropic Claude generates citation-backed answers.
- A multilingual embedding space enables cross-lingual document search without requiring explicit translation steps for queries.
Why it matters
Financial analysts often waste hours hunting through complex tables in massive SEC filings; this architecture shows AI engineering teams how to build low-latency, citation-accurate retrieval systems using enterprise-grade AWS and Cohere components.