Working with complex PDFs like user manuals, schematics, or multi-language logs? Checkout this benchmarking analysis of Retrieval-Augmented Generation (RAG) systems for Question Answering on Complex Industrial PDFs.

To support this, we built a modular ingestion and processing pipeline designed specifically for industrial documents, ranging from shift notes and engineering reports to scanned schematics and multilingual manuals.

Key contributions:

- A domain-adapted OCR + parsing stack optimized for noisy, heterogeneous documents

- Semantic chunking + entity linking, tuned for downstream QA performance

- A new benchmark: TIA-pdf-QA-Bench, which quantifies how OCR and chunking quality affect RAG-based QA

This pipeline is now available as a standalone module. If your work involves document-based reasoning, especially with scanned, structured, or noisy PDFs, we’d love to connect.

Sign up for early API access: https://lnkd.in/eu2C27gS

Have a tough use case? We’re particularly interested in collaborations involving low-quality scans, multimodal documents, or highly structured technical files. Reach out at solutions@thirdaiautomation.com