Amazing!!! We always want you to try it out with the "hardest" documents - So glad to hear it worked!

The goal of an effective document parser SHOULD be to help with the hardest problems. a low-quality scan of a Norwegian document with statistical tables from 1926 is the perfect test doc 🔥

We published:
✓ Full methodology
✓ Corrected OCRBench v2 ground truth
✓ Comparative analysis across all major providers

Read the full benchmark: tlake.link/benchmarks

Stop benchmarking vanity metrics. Start measuring what breaks.

The results were clear:

Tensorlake: 86.8% TEDS, 91.7% F1
AWS Textract: 80.7% TEDS, 88.4% F1
Azure: 78.1% TEDS, 88.1% F1
Docling: 63.8% TEDS, 68.9% F1

The gap? 670 fewer manual reviews per 10k documents.

We evaluated on OCRBench v2, OmniDocBench, and 100 real enterprise docs using two metrics that predict production success:

TEDS (Tree Edit Dist): Measures if tables stay tables
JSON F1: Measures if downstream systems can use the output

Not "is the text similar?" but "can automation actually work?"

Traditional benchmarks test on clean PDFs and measure text accuracy.

But your production failures come from:
- Collapsed tables
- Jumbled reading order
- Missing visual content
- Hallucinated extractions

None of this shows up in your scores.

Try it yourself with our SEC filing analysis notebook:
tlake.link/notebooks/vl...

Shows how to extract cryptocurrency metrics from 10-Ks and 10-Qs using page classification

Full changelog: tlake.link/changelog/vlm

What would you build with this?

Where we leverage VLM support:
📄 Page Classification: Large docs, specific sections needed
📊 Table/Figure Summarization: Visual data in reports
⚡ skip_ocr=True: When reading order is complex and for diagrams and scanned docs

Text extraction still uses OCR for best quality

Real results from analyzing 8 SEC filings:
- 1,500+ total pages
- 427 relevant pages identified by VLM
- Processing time: 5 minutes → 45 seconds per document

All without sacrificing accuracy

Our solution: VLMs understand document structure visually
Example: Extracting crypto holdings from SEC filings
1. VLM classifies which pages contain financial data (~50 out of 200 pages)
2. Extract only from relevant pages
3. Skip 70% of processing

Result: 80-90% faster ⚡

The problem: Processing 200-page documents when you only need specific information is slow and expensive

Traditional approach:
OCR everything → Convert to text → Search → Extract

This wastes time processing irrelevant pages

Build approval workflows that trigger on specific feedback. Extract complete edit history for regulatory compliance. Route documents based on flagged sections, all programmatically.

Live now in our API, SDK, and Cloud.

Now you can parse .docx files with tracked changes preserved as clean, structured HTML:
- <del> tags for deletions
- <ins> tags for insertions
- <span class="comment"> for reviewer notes

Perfect for:
→ RAG pipelines (better chunking)
→ Knowledge graphs (accurate trees)
→ Document navigation
→ Table of contents generation

Changelog: tlake.link/changelog/he...
Try it: tlake.link/notebooks/he...

Every section header now returns:
- level: 0 for #, 1 for ##, 2 for ###, etc
- content: clean text
- proper nesting for up to 6 levels

Enable with:
cross_page_header_detection=True

That's it.

Tensorlake analyzes numbering patterns (1, 1.1, 1.2) and visual structure across the ENTIRE document.

Then corrects misidentified header levels automatically.

Works even when headers span page breaks.

There's no reason your applications should not be citation-ready.

Dive deeper and try out the Colab notebook linked at the bottom of the blog

Step 3: Generating AI responses with verifiable citations

Once your chunks carry anchors, retrieval doesn’t change. You can use the dense, hybrid, or reranker setup you already have. Consider hiding the anchors in prose, while keeping them in output and making IDs clickable.

Step 2: Create contextualized chunks

Iterate through page fragment objects and create appropriately sized chunks by combining them. As you create the chunks, you can create contextualized metadata to help during retrieval.