OpenAsset

OpenAsset Achieves 40% Faster Query Handling with AWS-Powered LLM Solution.

Business Problem

OpenAsset needed to enhance its data query and retrieval process by integrating an LLM (Large Language Model) application that would:

  • Convert natural language queries into structured SQL-based responses.
  • Fetch accurate data from the Aurora DB to improve overall user experience.
  • Automate and streamline the response process using AWS infrastructure to reduce manual intervention.

About OpenAsset

OpenAsset is a project-based digital asset management (DAM) solution for real estate and AEC (Architecture, Engineering, and Construction) industries. It helps firms manage image libraries, streamline workflows, and create more efficient, high-performing proposals. With over 20 years of experience, OpenAsset has supported over 700 firms globally, enabling better visualization and presentation of projects, leading to increased business performance.

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Solution

The solution involved developing and deploying a prototype LLM application using AWS Bedrock to enhance OpenAsset’s data retrieval and response system. This application processes natural language queries, converts them into SQL queries, retrieves relevant data from Aurora DB, and generates natural language responses using AWS Bedrock’s Claude Sonnet LLM. The entire process was implemented with a well-structured tech stack:

OpenAsset
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Architecture

  • User Input
    – Accepts natural language queries and DB usernames.
  • Session History
    – Retrieves session history and merges it with current input.
  • Query Classification
    – Uses LLM to classify the domain and generate SQL queries.
  • SQL Execution
    – Makes API calls to AWS Bedrock for query generation.
    – Executes SQL queries to fetch relevant data.
  • Natural Language Response
    – Aggregates data and generates final user responses in natural language.
  • Database Update
    – Updates DB with query results and disconnects.
Outcomes

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 Efficiency Boost: Delivered a natural language interface, significantly reducing query handling time.

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Improved Data Accuracy: Generated highly accurate responses from Aurora DB, enhancing precision in information retrieval.

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 Increase in Customer Satisfaction: The ability to visualize outfits on their own photos significantly improved customer satisfaction, fostering brand loyalty and retention.

Technology Stack​