Optimizing Information Retrieval from Diverse Documents
Problem to be Solved
Users face difficulties in searching for and extracting information from various types of documents, leading to time loss and decreased work efficiency.
Problem Statement
How can users easily interact with and extract information from all types of documents quickly and accurately?
How QAI solution solves the problem?
Assistant allows users to upload any document format and interact through natural language Q&A, with the ability to understand context and extract accurate information.
Why the problem was not solved before?
- Traditional search solutions rely only on keywords.
- They lack the ability to process diverse document formats.
- They do not have the capability to understand context or link information.
Results from the solution
- Reduce search time by 70%.
- Increase information extraction accuracy by 90%.
- Improve user productivity.
3/4/2025
NEW
Crawl data from websites and use a large language model (LLM) to extract and summarize information that aligns with the user's needs.
NEW
Deploy a Retrieval-Augmented Generation (RAG) system on AWS using a serverless architecture to build an AI application capable of answering questions based on retrieved data. The solution allows users to upload documents, index the data, and interact through a web interface (built with Streamlit) to ask questions, with answers generated by combining information retrieval and the content generation capabilities of a large language model (LLM).
NEW
The Open Data QnA enables you to chat with your databases by leveraging LLM Agents on Google Cloud.
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