Challenge

Business departments often struggle with the manual effort required to navigate vast amounts of structured and unstructured internal documentation, such as complex credit policies and templates. This fragmentation leads to:

  • Slow risk evaluation cycles.
  • Difficulties in extracting specific data points from large document sets.
  • Inconsistent summarization of knowledge across different teams.


Approach

The solution implements a modular, AI-driven architecture on AWS, deployed over an 8-week timeline. Key technical components include:

  1. Centralized Knowledge Base: Leveraging Amazon S3 for unstructured data and Amazon RDS (Aurora Vector DB) for metadata and vector searches.
  2. Generative AI Orchestration: Utilizing Amazon Bedrock agents to perform automated summarization, data extraction, and risk evaluation.
  3. Secure Deployment: Using AWS App Runner for a scalable web interface, protected by IAM least-privilege roles and IMDSv2 security standards.
  4. Human-in-the-Loop (HITL): Integrating a validation workflow where analysts can verify AI outputs, ensuring high accuracy and reliability.
  5. Seamless Integration: Connecting the AI assistant directly into existing workflows via MS Teams and Salesforce.

Value Delivered

  1. Operational Efficiency: Automates the retrieval and summarization of credit documentation, significantly reducing the manual workload for analysts.
  2. Enhanced Risk Management: Centralizes access to internal knowledge, ensuring risk evaluations are based on the most current credit policies.
  3. Rapid Time-to-Market: A fully functional, customer-deployed environment is established in just 6 weeks of implementation time.
  4. Enterprise-Grade Security: Ensures all sensitive data is encrypted at rest and managed through strict AWS-native security protocols, avoiding root privilege risks.
  5. Cost-Effective Innovation: Delivers a sophisticated AI solution with a total project value of 9,900 USD, utilizing managed services to minimize operational overhead.

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