8,101 medical service rows. 99.8% extraction accuracy. 17 unmatched rows turned out to be office equipment mixed into the price list. Not AI errors - source data noise.
Construction estimates: 49K monthly searches in Russia. Compliance forms: 38K. Zero AI extraction companies ranking. We just published our first article.
Multi-model cascade: gpt-5.4-mini first pass + gpt-5.4 retry on low-coverage chunks. 99.8% accuracy at $0.80 for 8K rows. Full gpt-5.4 would cost $2.00.
Developers are becoming agent managers. Most have never been trained for it.
The bottleneck shifted: precise task formulation on input, fast quality review on output.
Most document AI platforms use one model for everything.
We route each document to the optimal model based on:
- Language and script detection
- Document complexity
- Table structure analysis
27 models benchmarked. No single best model exists. Only the right model for each job.
We benchmarked Arabic document OCR: Vision LLMs vs traditional engines.
Error rate (CER):
- Vision LLMs: 0.13
- Best traditional OCR: 0.54
- Worst: 0.79
4-6x quality gap. But VLMs have no bounding boxes.
Our solution: hybrid. VLM for text, traditional for coordinates.
We benchmarked our OCR on NVIDIA A10G (24GB VRAM) vs dedicated CPU.
1 page: 1.4s vs 19.6s (14x faster)
4 pages: 2.9s vs 23.9s (8x faster)
100+ languages. Sub-second per page on GPU.
Same accuracy. Different hardware tier.
Our approach: content-aware model routing.
Pre-scan -> detect language, complexity, type -> route to optimal model.
Multilingual docs? -> GPT-4.1-mini or DeepSeek V3
Simple English? -> Step 3.5 Flash (free, 5.8x faster)
Full data (27 models, CSV): link in bio.
We benchmarked 27 LLMs on real document extraction.
DeepSeek V3 (open source): 97.5% accuracy, 4.6x faster than GPT-4.1-mini.
GPT-5.3 Codex: 87.5%.
Open source just caught up. Full breakdown:
We open-sourced our document processing layer.
docfold: 15 engines, one Python API, MIT license.
Tesseract, PyMuPDF, Marker, Docling, PaddleOCR, AWS Textract, Google DocAI and more - one interface.
pip install docfold
#OpenSource#DocumentAI#Python
New: AI Dashboards & Analytics.
Upload docs. Extract data. Ask questions in plain English - get charts and insights in seconds.
No SQL. No BI developer. No setup.
Available for Business & Enterprise plans.
https://t.co/eWYlaoF83D
#DocumentAI#AIAnalytics#EnterpriseAI
In enterprise data processing, "good enough" is not good enough.
99% extraction accuracy. Governed. Traceable. Auditable.
When your compliance team asks "where did this number come from?" - you need an answer.
https://t.co/8keHzgX1LM gives you that answer.
A pump manufacturer used to spend 3 days analyzing a single tender.
With https://t.co/8keHzgX1LM: 15 minutes. Same accuracy. Full audit trail.
That is what enterprise AI should look like - not chatbots, but governed data pipelines that save real time.
https://t.co/N9neS0275p
Not a file converter. Not a chatbot. A governed data pipeline.
Collect -> Enrich -> Govern -> Analyze
One platform. Four modules. From raw documents to boardroom decisions.
https://t.co/yL3i16ixeJ