Academic benchmarks ≠ business impact. Real enterprise success means handling PDFs and docx, pptx, eml, msg, tiff, epub, xlsx… with fidelity, fallback, and scale. That’s where Unstructured shines. Join our next webinar on what benchmarks should actually measure →.

RT @UnstructuredDan: The Document AI space has seen a fundamental shift in the past year. Everyone—from scrappy startups to established pla….

Why are complex tables so hard to parse?. OCR can detect characters, and some newer models can even handle simple tables. But once you introduce blank cells, multi-row headers, or nested structures, OCR quickly falls short. Rows and columns lose their positionality, context

Handwritten forms? Tilted scans? Messy docs? We love the hard stuff. Check out how our partitioner handles it → Next week, @UnstructuredDan is taking a deeper dive in our webinar, Pushing the Boundaries of Document Transformation Quality. Sign up here to.

There's still time to sign up for today's webinar! Join us in just a few minutes 👇.

There's still time to sign up for tomorrow's webinar! You won't want to miss this one. 🔗

In our latest webinar, we dug into what evals are, why they matter, and how they’re continuously evolving in the GenAI landscape. Evaluation has shifted beyond task accuracy to include benchmarking across models, measuring reliability, tracking costs, and more. And in this

Want to learn more? Join us this Wednesday for a live webinar on how we extract structured, contextual data from complex tables without losing fidelity, meaning, or structure. Sign up today 👉

📝 Check out our latest blog post to dive deeper into our approach: 🎙️ Learn more in our upcoming webinar where we discuss how we achieved industry leading document transformation quality: #DocumentAI #HTML #VLM #Ontology.

And finally, the proof is in the eval — in our benchmarks, our VLM partitioner consistently outperforms other VLM-based parsers on the market, even when using the same models!. This is why we believe HTML is the future foundation of document transformation. 5/🧵.

And because fidelity alone isn’t enough— predictability and repeatability are also critical, we defined a 70-document element ontology to constrain the entire set of HTML vocabulary to a well-defined subset, ensuring reliability with our transform. This means a figure caption is.

So our thesis: HTML isn’t just a web format—over time it will become the canonical layer of Document AI. It will bridge how models learn with what enterprises demand with their information representations: fidelity, structure, auditability, interlinkability & flexibility. 3/🧵.

First of all, it’s the most expressive, enterprise-ready format for representing documents — not to mention it's literally used by the entire internet. But on top of that, it features:.- Model-native: VLMs have been trained on billions of HTML↔visual mappings. They already.

Most vendors output JSON or Markdown. We chose HTML—not as a convenience, but as a thesis both about the representation language the foundation models best understand as well as where document AI is heading. Why HTML? 1/🧵

ETL should be as reliable as turning on the tap. In a recent webinar, we dug into why consistency in your data pipelines matters, and how Unstructured makes it easier to get clean, structured data to power your GenAI applications. Watch the full recording here:

btw, have a particular model or agentic strategy you’re curious about? evaluation metric? what has been a dead end or an unlock? drop a comment below so we can cover it soon! 7/🧵. #TableTransformation #DocumentAI #VLM #StructuredData #DataQuality #RAG #AI #GenAI #ETL.

If your AI can’t parse them correctly, every downstream system—RAG, analytics, compliance—fails. That’s why we treat table transformation not as a feature, but as a foundation. Come talk with us about it! Join us next Wednesday 9/3 for our upcoming webinar: How to Extract Data.

But excellence doesn’t come from a single clever prompt. We’ve poured countless R&D hours into prompt design, ontology modeling, and routing logic across every major foundational model. That’s what separates production-grade transformation from demo-level parsing. The result:.

When Vision Language Models emerged, they upped the ante. Suddenly, it became possible to tackle some of the hardest table features:.- Merged cells that maintain alignment.- Multi-row and multi-column headers.- Nested structures across multi-page layouts. 4/🧵.

Simple “accuracy” wasn’t enough. To evaluate real performance, we built a framework that measured:.- Object detection quality: were tables, rows, and cells segmented correctly?.- Structural integrity: did rows and columns align, with no shifts or gaps?.- Content fidelity: were.