🚀 A PDF Parsing Method That Outperforms AI by 1000×
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🚀 A PDF Parsing Method That Outperforms AI by 1000×
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Parsing PDFs at enterprise scale is notoriously hard — metadata is missing, structures are inconsistent, and AI models often slow to a crawl when you throw millions of documents at them.
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Parsing PDFs at enterprise scale is notoriously hard — metadata is missing, structures are inconsistent, and AI models often slow to a crawl when you throw millions of documents at them.
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In a recent blog post, I shared how we can use data lineage to  process data in PDFs 1000× faster than AI approaches, since PDFs are almost always the result of exporting from some other format. 
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In a recent blog post, I shared how we can use data lineage to  process data in PDFs 1000× faster than AI approaches, since PDFs are almost always the result of exporting from some other format. 
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One way to do this:
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One way to do this:
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✅ Parse all non‑PDF documents first,
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✅ Parse all non‑PDF documents first,
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✅ Build a RAG (retrieval‑augmented generation) index from them,
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✅ Build a RAG (retrieval‑augmented generation) index from them,
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✅ Perform simple (non‑AI) parsing on PDFs and cross‑check against the RAG,
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✅ Perform simple (non‑AI) parsing on PDFs and cross‑check against the RAG,
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✅ Only fall back to AI parsing if the data isn’t already known.
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✅ Only fall back to AI parsing if the data isn’t already known.
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Another is to use a lineage-based tool like we developed at Caber.
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Another is to use a lineage-based tool like we developed at Caber.
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👉 Read the full deep dive and try the code yourself here:
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👉 Read the full deep dive and try the code yourself here:
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https://www.caber.com/blog/2e0a903d-caa2-4e93-a3a6-94636ec5ee2e
https://www.caber.com/blog/2e0a903d-caa2-4e93-a3a6-94636ec5ee2e
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If you’d like to reproduce the results, the blog post includes runnable code and step‑by‑step instructions. I’d love to hear how you handle PDF parsing and document processing in your projects — feel free to share your approaches or improvements in the comments.
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If you’d like to reproduce the results, the blog post includes runnable code and step‑by‑step instructions. I’d love to hear how you handle PDF parsing and document processing in your projects — feel free to share your approaches or improvements in the comments.
🚀 A PDF Parsing Method That Outperforms AI by 1000×

Parsing PDFs at enterprise scale is notoriously hard — metadata is missing, structures are inconsistent, and AI models often slow to a crawl when you throw millions of documents at them.

In a recent blog post, I shared how we can use data lineage to  process data in PDFs 1000× faster than AI approaches, since PDFs are almost always the result of exporting from some other format. 

One way to do this:
✅ Parse all non‑PDF documents first,
✅ Build a RAG (retrieval‑augmented generation) index from them,
✅ Perform simple (non‑AI) parsing on PDFs and cross‑check against the RAG,
✅ Only fall back to AI parsing if the data isn’t already known.

Another is to use a lineage-based tool like we developed at Caber.

👉 Read the full deep dive and try the code yourself here:
https://www.caber.com/blog/2e0a903d-caa2-4e93-a3a6-94636ec5ee2e

If you’d like to reproduce the results, the blog post includes runnable code and step‑by‑step instructions. I’d love to hear how you handle PDF parsing and document processing in your projects — feel free to share your approaches or improvements in the comments.
👀
John6666 caborabo