Lenntech, an expert in designing and providing customized water treatment solutions, has entered into a collaboration with Polarix Data and Sping BV to develop an advanced AI assistant specifically designed to radically transform the way Lenntech manages its extensive project documentation and product files.

Sophisticated Data Integration

The journey begins with transforming over 800,000 product-related documents from static PDF files into dynamic, searchable vectors. This vectorization process is crucial, enabling the AI to perform deep, contextual analyses of vast amounts of data swiftly.

Intelligent Query Processing

Queries received via email are converted into vectors using the same sophisticated technology. This standardization into vector form allows for seamless integration and comparison with the project data already processed in the AI system.

Enhanced Data Retrieval

The core of this system is a Retrieval-Augmented Generation (RAG) database, where both project data and incoming queries are matched. This AI-powered matching process identifies the ten most relevant project documents for any given query, ensuring that responses are both quick and pertinent.

Multi-Dimensional AI Analysis

Once relevant documents are identified, they are fed into a large language model (LLM) for further refinement. This LLM, supported by four specialized AI agents, conducts a comprehensive analysis. These agents are tasked with:

• Analyzing the context and details within the data.
• Verifying the accuracy and relevance of the information.
• Providing advice on potential product applications.
• Assessing risks associated with the proposed solutions.

Tailored and Informed Client Responses

The outcome of this intricate process is a detailed, accurate response to the client query, including recommendations on appropriate products and detailed explanations of their suitability. All advice is substantiated with references to the related project files, which are attached for the client’s review. This approach not only streamlines the retrieval and analysis of data but also transforms the client interaction model from one of passive consultation to active, informed decision-making support.