AI-enabled document search solution
Efficient document management and information retrieval are critical for organizations that work with complex processes like mergers and acquisitions. Advanced technological solutions that use Artificial Intelligence (AI) can refine document search, classification, and access, and thus reduce time spent on information sorting.
Our client is a Virtual Data Room (VDR) provider that secures confidential document sharing and corporate deal management. They serve businesses during complex deal-making processes and offer solutions for confidential information exchange and management. They turned to us with plans to improve document content analysis.
Solution
Unidatalab developed an AI-powered document search and classification system to upgrade the client's existing service offering. We focused on two key technological innovations: intelligent document categorization and conversational document search.
How it works
Document embedding preparation. Each document in the virtual data room undergoes a transformation process where its content is converted into specialized vector representations called embeddings. These mathematical representations capture the semantic meaning of documents, which allows for nuanced and context-aware searching.
Intelligent vector database. The created embeddings are stored in a purpose-built database with advanced search algorithms. This specialized database enables rapid, precise retrieval of relevant information based on semantic similarity rather than simple keyword matching.
When a user asks a question, the system follows a multi-step approach:
The query is first converted into an embedding
The system searches the vector database to find the most relevant document segments
Retrieved information is then used to enrich the original query
A Large Language Model generates a context-specific answer drawing from the retrieved documents
Our challenges:
Complex document management
During high-stakes transactions like mergers and acquisitions, managing and quickly accessing large volumes of confidential documents was increasingly difficult and time-consuming.
Lack of clear document categorization
Since users often struggled to locate and retrieve specific documents, the existing system needed a new method for the automatic categorization and organization of documents.
Project stages
Our team conducted meetings with the client to clarify their requirements and expectations. We focused on their needs for an AI-powered document search and classification system. Our primary objectives were to define the exact scope of work and develop a strategic roadmap.
We initiated data exploration and transformation activities, working closely with the client to establish clear parameters around document types and system limitations. Our technical experts conducted an in-depth investigation to identify and select the most suitable models for information retrieval and relevant information selection. This step involved rigorous evaluation of various algorithmic approaches to ensure optimal performance in supporting Large Language Models (LLMs) during the question-answering process. Simultaneously, we focused on the selection and testing of appropriate LLMs.
Unidatalab implemented a conversational document search pipeline. This pipeline incorporated components for both context extraction and question-answering functionalities. Our team evaluated the implemented solution by testing it across multiple documents with predefined questions. The final deliverable was a live demonstration that showcased the system’s capabilities.
Our experts refined the high-level solution architecture based on insights gained from the PoC. Our development team created a web service to host the conversational document search functionality. We enhanced the solution’s infrastructure by Dockerizing the application, which improved its portability and scalability. Critical technical work implied the integration of APIs and installation of connections with databases for seamless data retrieval and storage.
Summary
