The world today is quite different though. In the last 24 months, the "TAM" for document processing has expanded by multiple orders of magnitude. In the next 10 years, trillions of pages of documents will be ingested across all verticals.

Previous generations of tools were always limited to the same set of structured/semi-structured documents (e.g. tax forms). Today, engineering teams are ingesting truly the wild west of documents, from 500pg mortgage packages to extremely messy healthcare forms. All of those legacy providers fall apart when tackling these types of actual unstructured docs.

We work with hundreds of customers now, and I'd estimate 90% of the use cases we tackle weren't technically solvable until ~12 months ago. So it's nearly all greenfield work, and very rarely replacing an existing vendor or solution already in place.

All that to say, the market is absolutely huge. I do suspect we'll see a plateau in new entrants though (and probably some consolidation of current ones). With how fast the AI space moves, it's nearly impossible to compete if you enter a market just a few months too late.

We've seen customers integrate these in a few interesting ways so far:

1. Agents (exposing these APIs as tools in certain cases, or into a vector DB for RAG)

2. Real-time experiences in their product (e.g. we power all of Brex's user-facing document upload flows)

3. Embedded in internal tooling for back-office automation

Our customers are already requesting new APIs and capabilities for all the other problems they run into with documents (e.g. fintech customers want fraud detection, healthcare users need form filling). Some of these we'll be rolling out soon!

A lot of customers choose us for our handwriting, checkbox, and table performance. To handle complex handwriting, we've built an agentic OCR correction layer which uses a VLM to review and make edits to low confidence OCR errors.

Tables are a tricky beast, and the long tail of edge cases here is immense. A few things we've found to be really impactful are (1) semantic chunking that detects table boundaries (so a table that spans multiple pages doesn't get chopped in half) and (2) table-to-HTML conversion (in addition to markdown). Markdown is great at representing most simple tables, but can't represent cases where you have e.g. nested cells.

You can see examples of both in our demo! https://dashboard.extend.ai/demo

Accuracy and data verification is challenging. We have a set of internal benchmarks we use, which gets us pretty far, but that's not always representative of specific customer situations. That's why one of the earliest things we built was a evaluation product, so that customers can easily measure performance on their exact docs and use cases. We recently added support for LLM-as-a-judge and semantic similarity checks, which have been really impactful for measuring accuracy before going live.

Our goal is to provide customers with as much flexibility as possible. For certain use cases, you might be willing to take a slight hit to accuracy in exchange for better costs and latency. To support this, we offer a "light" processing mode (with significantly lower prices) that uses smaller models, fewer VLMs, and more heuristics under the hood.

For other use cases, you simply want the highest accuracy possible. Our "performance" processing mode is a great fit for that, which enables layout models, signature detection, handwriting VLMs, and the most performant foundation models.

We back this up with a native evals experience in the product, so you can directly measure the % accuracy difference between the two modes for your exact use case.