Ledgebrook AI Chatbot for Ledgebrook’s Underwriting Workbench

Business Problem

  • Inefficient Data Retrieval: Underwriters had to manually search for relevant guidelines, wasting time and slowing decision-making. 
  • Delayed Underwriting Decisions: The lack of a centralized, conversational interface led to inefficiencies in handling queries. 
  • Scattered Document Access: Important documents were stored across multiple systems, making retrieval cumbersome. 
  • Scaling Challenge: As the number of underwriting requests increased, manual processes became unsustainable. 

About Ledgebrook

Ledgebrook’s underwriters needed a way to quickly access underwriting guidelines and compare them with submitted documents. goML developed an AI-powered chatbot to streamline the underwriting process. 

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Solution

goML developed an intelligent chatbot powered by AI and search capabilities: 

1.

Conversational Interface for Underwriters: A chatbot was built using AWS Bedrock, enabling underwriters to retrieve policy and document information via natural language queries. 

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Seamless API Integration: The chatbot communicated with Ledgebrook’s existing document processing systems via AWS Lambda. 

2.

Automated Document Lookups: Integrated with OpenSearch Serverless, the chatbot fetched relevant documents instantly. 

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Secure & Scalable Deployment: Hosted within a VPC-based AWS infrastructure, ensuring data security and high availability. 

3.

Policy & Document Comparison: Underwriters could compare guidelines with submitted documents stored in AWS S3 and categorized using PostgreSQL RDS. 

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Search & Retrieval Optimization: Document vectorization in OpenSearch improved response accuracy for underwriting queries. 

Architecture

  • User: The initiator of the process, providing an input payload containing a token and a query. 
  • DB (Database): Stores user data, including the aiDocumentSessionToken, potentially used for authentication or session management.
  • Bedrock: Serves as the central component for orchestration and processing, handling:  
  • Prompt Engineering: Formulating prompts for the RAG pipeline. 
  • RAG (Retrieval Augmented Generation): The core logic for retrieving relevant information and generating responses. It interacts with:  
    S3 (Simple Storage Service): Stores the document corpus (underwriting data, document information). 
    OpenSearch: Provides indexing and search capabilities over the document corpus for efficient retrieval. 
  • Webhook Endpoint: An external service that is triggered by Bedrock to deliver the final response. 
  • Input Payload: The user sends an input payload (token, query) to the DB. 
  • Token Retrieval: The DB verifies the token and provides the aiDocumentSessionToken to Bedrock. 
  • RAG Pipeline Execution: Bedrock uses the token and query to initiate the RAG pipeline. This involves:  
  • Prompt Engineering: Creating a suitable prompt for querying the document index. 
  • Retrieval: Querying OpenSearch to fetch relevant documents from S3. 
  • Information Extraction: Processing the retrieved documents to extract the necessary information to answer the query. 
  • Response Generation: Bedrock generates a final response based on the extracted information. 
  • Webhook Trigger: Bedrock triggers the Webhook endpoint, passing the final response. 
  • Final Response Delivery: The Webhook endpoint delivers the final response to the user. 
Outcomes

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reduction in time spent retrieving underwriting data by enabling instant chatbot responses. 

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improvement in user efficiency by allowing underwriters to access critical information without manual searches. 

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faster decision-making through real-time interaction with the system’s backend.