To address the inefficiencies in manually classifying vast customer comments for identifying gas leaks, our team developed a robust framework powered by LLaMa LLM. The solution automatically classifies comments as “Gas Leak” or “No Gas Leak,” enabling faster issue resolution, improving safety measures, and laying the foundation for predictive maintenance models.

• The client faced a deluge of unstructured comments, making manual classification time-intensive and error-prone.
• Customer comments often contained domain-specific jargon, abbreviations, and regional nuances.
• Historical data had varying formats and inconsistent labels, complicating training and classification.
• Manual classification delayed the resolution of critical safety issues like gas leaks.
• The system needed to handle large-scale data efficiently as the client’s operations expanded.

• Designed a custom pipeline utilizing LLaMa LLM to process and classify unstructured textual data.
• Integrated pre-trained LLaMa models fine-tuned with domain-specific datasets for high accuracy.
• Cleaned and normalized historical data to standardize formats.
• Enhanced the training dataset with labeled examples for “Gas Leak” and “No Gas Leak” categories.
• Built the classification system on a cloud-based architecture to handle high volumes of data seamlessly.
• Integrated APIs for real-time classification and feedback loops for continuous learning.
• Automated the data ingestion, processing, and classification workflows to reduce manual effort.
• Implemented visual dashboards for engineers to access categorized comments quickly.
• Designed the framework to feed classified data into predictive maintenance models for proactive issue management.