If you've ever tried to extract data from documents at scale, you know the pain. OCR tools misread numbers with unshakeable confidence. LLMs hallucinate clauses that don’t exist. And don't get us started on trying to parse a PDF that was clearly designed by someone who hates technology.

In an AI-first world, these failures cascade. What used to be a minor data hiccup now breaks your entire AI strategy. When systems move fast and markets are fragile, 95% accuracy isn't good enough—you need 100%.

Why Everyone Suddenly Cares About Document Processing

Several converging trends are happening that make document intelligence way more critical than it used to be:

• Training data is running out: High-quality training data could be exhausted by 2026, making precision-curated data the next frontier in AI performance. When document processing pipelines feed corrupted or incomplete data into training sets, those errors propagate through every downstream model. 
• ‍AI agents are everywhere, and they're making real decisions: AI hallucination rates hit 48% in 2025. When an agent acts on completely made-up document data, things can go very wrong very fast. Your agent needs to trust its inputs, or you're basically playing Russian roulette with your business.
• ‍Bad data is expensive: On average, U.S. enterprises lose $12.9 million annually to poor data quality. Imagine layering AI on top of that mess. 77% of businesses cite LLM hallucinations as a major concern, and for good reason—hallucination rates can hit 27% in general use and 82% in sensitive areas like legal documents. One hallucinated clause or misread number can trigger compliance failures, fines, and painful disruption.‍
• AI-First Products are the gold standard: Companies like Gamma build their entire product around fast, accurate extraction. In today’s market where switching costs are low and UX defines success, slow or unreliable document processing translates directly into people leaving for your competitor.

The Current Document Processing Dilemma

Today’s document pipelines typically face three options: OCR, LLMs/VLMs, or human-in-the-loop parsing. None of these solutions work for modern AI-first workflows or scale confidently across millions of documents. 

OCR tools like Tesseract and ABBYY were built for simpler times. They fall apart when you throw complex layouts, tables, or inconsistent formats at them—which is basically every document that matters.

In response, a new wave of tools now wrap general-purpose language models to produce structured outputs. But these general-purpose models were never designed for deterministic extraction. They hallucinate plausible-seeming values that weren't actually present in documents, making them risky for high-stakes workflows. Some solutions try to fix this with prompts or post-processing, but the edge cases—messy inputs, broken layouts, subtle structure—are exactly where these systems fail.

What We’ve Built at Datalab

At Datalab, we’ve spent a lot of time in the weeds on this problem. We’ve already processed over 250M pages and used that experience to build the best models for document parsing: fast, accurate, and robust in the wild - even when documents are messy, chaotic, or multi-lingual.

We obsess over two things:

1. Accuracy — no hallucinations, no skipped content.
2. Throughput — fast enough to scale across millions of documents.

We do this through custom model architectures that are specifically designed for documents. 

Here’s a peek at just a few of our custom architectural tweaks:

• UTF-16 tokenization, giving us consistent compute per character across 90+ languages, covering 20+ scripts.
• Bounding boxes for every character, word, line, and block - Our models are spatially aware, and can output highly accurate bounding boxes, so you know exactly where your data came from.
• Line-level extraction, which gives us higher parallelism (better throughput), lower hallucination surface, and better accuracy on complex, multi-lingual documents. 
• Native Resolution Image Processing, which helps us stay accurate, and token-efficient, even when handling lines, blocks, and pages of wildly varying resolutions and aspect ratios.
• Custom table models that break tables into logical units - our table models can figure out exactly where each cell is, and how it relates to the cells around it.
• Selective refinement through LLMs - our architecture allows us to selectively call on LLMs to improve accuracy while still minimizing hallucination risk.

A big part of why this works is that our product and models are developed hand-in-hand.

We don’t train models in isolation - every architecture decision, every dataset tweak, is grounded in the problems we see in the wild. This feedback loop is tight: product usage informs model design, and model improvements unlock new product capabilities.

At Datalab, PDFs are a primary input modality. Our extraction is tightly grounded by the underlying PDF text. When it’s available and usable, we extract it. When it’s broken (which is often), we fall back to vision. Our models are aware of this text, and can exit early when the extracted content is good - meaning we don’t waste compute regenerating what’s already there. 

This gives us:

• Higher accuracy - because we avoid hallucinating what we already have.
• Higher throughput - because we don’t redo work we don’t need to.
• Robust extraction - because our fallback plan is smart and adaptive.

Don’t just take our word for it. These are some of the benchmarks that led Tier 1 research labs and Fortune 500 companies to choose Datalab:

Check out more of our benchmarks here.

Deploy accurate document intelligence in minutes

We make it easy to deploy:

• On-prem → Full control over your data, fast offline parsing.
• Hosted API → We manage the infra (99.998% uptime nbd), you focus on building.

We also built an interactive dashboard where you can test your use cases and audit model outputs directly. Click through your documents, validate results, and iterate on your workflows with full transparency.

If you want to see how this works, get in touch or check out our open-source repos (42K+ stars and counting).

(If you made it this far without asking Claude to summarize this post, congratulations. Email us with “I used my own neural networks” and we'll hook you up with free credits!)