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03
Enterprise Financial RAG Assistant
Capital One · Professional
A Retrieval-Augmented Generation assistant built for financial analysts to query internal documents, policies, and reports. It improves productivity and ensures accuracy and compliance through grounded answers, source citations, and responsible-AI guardrails — over 2M+ compliance documents.
RAGLLMLangChainPineconeAWS BedrockClaudeSecureScalable
Purpose
Grounded answers from internal financial docs
Impact
~46% less analyst research time
Users
Financial analysts & compliance teams
Corpus
2M+ policy & compliance documents
System Architecture
Data Sources
PoliciesReportsFilings (PDF/DOCX)Ingestion
ParsingChunkingEmbeddings(Vector DB) →
Retrieval & QA
HybridRetrieverReranker
(Bedrock) →
GuardrailsAnswer +
Citations
Citations
RAG Process Flow
1
Ingestion
Documents are ingested from multiple internal sources and parsed.
2
Chunking
Text is split into small, meaningful segments with metadata.
3
Embedding
Chunks are converted to embeddings and stored in Pinecone.
4
Retrieval
A query is embedded; hybrid search + reranking fetch the most relevant context.
5
Generation
Claude (via Bedrock) generates an answer grounded in the retrieved context.
6
Response
The answer is returned with source citations and passed through guardrails.
Key Features
- Hybrid search (keyword + semantic) with reranking
- Grounded source citations & transparent responses
- Hallucination mitigation with prompt guardrails
- Responsible-AI validation for compliance workflows
- Scalable, modular, production-ready design
Tech Stack
LangChainLlamaIndexPineconeAWS BedrockClaudeSentence TransformersFastAPIDockerKubernetes
Impact
~46%
Less Research Time
2M+
Documents Indexed
Top-k
Hybrid Retrieval
Cited
Grounded Answers
Sample Query
Q: What is the policy for expense reimbursement for travel?
A: Per the Travel & Expense Policy, employees submit reimbursement within the stated window after travel completion...
Sources:
1. travel_policy.pdf (p. 12)
2. expense_guidelines.pdf (p. 8)