Timeframe & Required Resources
• Connect existing document repositories to the Vauban platform via the API.
• Set up the search rules and response generation configurations.
• Test the system with queries to validate the relevance of the results.
Benefits
• Accuracy: Key information is extracted directly from relevant documents, ensuring its accuracy.
• Simplicity: A single API to query and retrieve enriched responses.
• Speed: Instant access to data, even in large repositories.
• Security: Data management and processing in compliance with the highest standards.
• Efficiency: Free your teams from manual search tasks, allowing them to focus on strategic activities.
Companies accumulate vast amounts of documents (contracts, reports, manuals, standards), making quick and accurate access to key information challenging. Manual searches are time-consuming, inefficient, and increase the risk of errors or missed information, hindering decision-making.
By using advanced AI solutions such as Retrieval-Augmented Generation (RAG RAG) combined with Language Models (LLM LLM), businesses can automate document retrieval and generate contextually relevant answers in real-time, significantly improving efficiency, reducing errors, and accelerating the decision-making process.
Query Massive Document Volumes with Text Generation (LLM + RAG)
Get Clear and Accurate Answers from Vast Document Databases with the Power of RAG and LLM
Solution Vauban
• RAG (Retrieval-Augmented Generation RAG): Identifies and extracts relevant information from internal documents.
• Text Generation API: Creates synthetic and contextually relevant responses based on extracted data.
• Embeddings API: Structures and analyzes documents to enhance search relevance.
These solutions work together to streamline document processing, enabling businesses to efficiently retrieve, process, and generate insights from vast amounts of data.
How It Works
1. Document Indexing: Internal documents are integrated into the system using RAG (Retrieval Augmented Generation), making them queryable in real-time.
2. Query and Retrieval: A question is posed via the API, and RAG (Retrieval Augmented Generation) identifies the most relevant passages within the documents.
3. Response Generation: The LLM (Large Language Model) generates a contextualized response by combining the extracted information with its semantic interpretation.
This process ensures efficient, accurate, and quick retrieval of relevant data from large document sets.
Real-World Example
• A government institution used RAG RAG and LLM LLM to provide precise answers to citizens by instantly consulting legislative texts and circulars.
• A defense company queried thousands of technical manuals to assist field teams in troubleshooting critical equipment.
• A bank automated the analysis of loan contracts by extracting key clauses in seconds, improving compliance and reducing audit times.
Industries
Software and SaaS AI
Public Sector
Industrial Sector
Retail and E-commerce
Education
Aerospace
Energy Sector
Automotive Sector
Healthcare Sector
Telecommunications
Advertising and Marketing
Video Games
Financial Services
Products Used
Corporate LLM
AI solution unifying LLM and RAG, leveraging your internal data for strategic needs.
Rerank
AI solution that optimizes your searches with a relevance score for filtering, ranking, and rerank optimization.
Translate
AI Solution for Precise and Rapid Translation, Tailored to Industry Specifics