Challenges

A financial advisory firm relied on manual processes for document retrieval and information extraction. These processes lead g to several operational challenges such as:

• Time-Consuming and Labor-Intensive retrieval: Retrieving information from documents involved manual searching and scanning, which consumed significant time and effort.
• Structured Knowledge: Used Amazon S3 to store extracted text in an organized format.
• Low accuracy: Manual methods often resulted in inconsistencies and inaccuracies in information retrieval, impacting decision-making and client interactions.
• Reduced efficiency: The reliance on manual processes hampered operational efficiency, causing delays in accessing critical information needed for client servicing and compliance purposes.
• Unscalability: As the volume of documents grew, manual processes became increasingly unsustainable, leading to scalability challenges and potential bottlenecks in workflow.
• Manual oversight: Manual retrieval increased the risk of missing critical regulatory information, potentially leading to compliance issues and legal ramifications.

Searce Solution

Searce provided a solution that leveraged AWS services including Amazon Textract, Comprehend, SageMaker, S3, and AWS Lambda, integrating them to create a seamless document-scanning chatbot powered by the Amazon Bedrock model. This helped achieve:

• Secure and scalable document storage: Documents are securely stored in Amazon S3, ensuring easy accessibility and scalability.
• Automated text & data extraction: Amazon Textract is utilized to extract text and structured data from scanned documents stored in Amazon S3.
• Intent-Driven document search: Amazon Comprehend processes user queries to understand the intent and extract key entities for accurate document retrieval.
• Custom AI model for advanced retrieval: The Amazon Bedrock model, specialized in document analysis and information retrieval, is trained and deployed on Amazon SageMaker.
• Automated workflow orchestration: AWS Lambda functions orchestrate the workflow, invoking the deployed Amazon Bedrock model based on user queries submitted through Amazon Lex.
• Intelligent information retrieval with chatbot: Upon identifying the document(s) containing the relevant information, the chatbot retrieves and highlights specific sections for the user.
• Seamless integration & user interface: Integrated the chatbot with the financial advisory firm's existing systems to provide a user-friendly interface for seamless interaction and retrieval of document-based information..

Business Impact

• Effortless information retrieval: The automated chatbot significantly reduces the time and effort required to retrieve information from documents.
• AI powered precision: Utilizing the Amazon Bedrock model ensures precise extraction and retrieval of relevant data points.
• Enhanced User Experience: Users benefit from quick and accurate responses to their queries, improving overall satisfaction and productivity.

Our solution empowered the financial advisory firm to revolutionize client service through intelligent document retrieval. By leveraging AI and automation, they are now able to focus on client relationships, while maximizing efficiency and ensuring compliance.