The Challenge
A major utility provider was relying on manual transcription of photographed nameplate tags from transformers and circuit breakers. This process was time-consuming, error-prone and inconsistent. The wide variety of tag formats added complexity and transcription errors were common, especially during urgent maintenance or emergency scenarios. Accurate, real-time data entry was critical but difficult to achieve with manual processes.
How We Helped
Experis developed a Minimum Viable Product (MVP) that combined Optical Character Recognition (OCR) with a Large Language Model (LLM) to automate the entire data extraction process.
The AI-powered OCR scanned and processed images of transformer and circuit breaker tags, extracting text with high accuracy, even in poor lighting or from worn surfaces. The LLM then interpreted, standardized and structured the data, flagging any unclear entries for manual review.
The output was formatted in JSON (JavaScript Object Notation) and delivered in a consistent JSON schema, securely integrated with the client’s asset management systems to ensure real-time updates and data integrity.
The Impact
The AI-driven solution delivered measurable improvements in both speed and accuracy.
- Reduced transcription errors and improved data consistency
- Enabled real-time data entry with near-instant database integration
- Freed up staff to focus on higher-value tasks by automating routine data capture
- Improved asset visibility and decision-making through accurate, up-to-date records
This MVP laid the foundation for scalable automation and smarter asset management in a highly dynamic operational environment.
