AI Specification Extraction for Valve Documents

An industrial engineering client had engineers spending entire days pulling specification data out of valve datasheets. Thousands of PDFs came from hundreds of manufacturers, and the layouts did not match. Engineers were typing out pressure ratings, material types, and connection sizes. The work was slow and easy to get wrong.

Appoly built a document-understanding pipeline using OCR, computer vision, and a large language model. An engineer uploads one datasheet or a folder of them. The system returns clean, validated, structured data the team can search, and the source snippet for each value, so a person can check a result that looks wrong.

Manual extraction that used to fill a senior engineer's day now runs in the background. Extraction time fell by 95 percent. The pipeline processed 10,000 or more documents in days. The structured output feeds procurement and project tooling. The same pipeline is being extended to other document types.

A senior team does the work from South Melbourne, and delivery is human-led and AI-accelerated. Working software reaches users every fortnight. The client owns the code, infrastructure, and documentation. We hand those over without lock-in or bespoke frameworks.

Turning a folder of datasheets into structured records sits with AI Integration. A source snippet on each value sits with Retrieval-Augmented Generation (RAG). Building this kind of pipeline sits with AI Enablement.