AI-Powered VOCs Report Generation for an Environmental Testing Laboratory
Our client is an environmental and occupational health testing laboratory that specializes in delivering precise, reliable chemical analysis services.
They faced challenges in manually interpreting complex lab data and generating insightful Volatile Organic Compounds (VOCs) reports, which is a time-consuming and expertise-driven task.
Solution
Unidatalab developed an AI-powered system that uses advanced LLMs and a RAG framework to semi-automate report generation. This solution leverages historical data, applies advanced prompting techniques like Chain-of-Thought, and integrates seamlessly into existing workflows via a REST API, while maintaining human-in-the-loop validation to ensure precision and reliability.
How it works
The system processes structured and unstructured data from lab reports and sampling details.
By tapping into previous reports and a curated knowledge base, the system enhances context and relevance.
The LLM drafts the Volatile Organic Compounds (VOCs) report with clarity and domain awareness.
The dockerized solution easily integrates with existing workflows, enhancing scalability and flexibility.
Our challenges:
Manual and time-consuming interpretation
Generating accurate VOCs reports required significant expert input, slowing down the process and risking inconsistencies.
Complex data handling
Effectively interpreting tabular data and ensuring consistency across reports was challenging and limited scalability as report volumes increased.
Project stages
An initial prototype was developed to validate the feasibility of using LLMs for report generation. This phase focused on integrating core components, understanding data workflows, and ensuring the system could process and generate reports based on provided data.
Building upon insights from the PoC, the MVP phase enhanced system robustness, incorporated additional functionalities, and prepared the solution for deployment. Key developments included advanced prompting techniques, integration of supplementary knowledge bases, and the establishment of endpoints for dynamic vector database extension.
Finally, our team refined all components and completed the full integration into our client’s platform. We conducted extensive testing of the image generation, document parsing, and CV analysis features, optimizing their performance based on real-world data and user feedback. This phase concluded with the deployment of all new features, followed by close monitoring and support to support smooth operation within our client’s existing workflow.
Summary
