Hirize

Introducing Agentic OCR from Hirize.
Most OCR systems are built for clean, structured documents. The moment you introduce handwriting, faded text, stamps, or overlapping annotations, accuracy drops and pipelines break.

Agentic OCR changes that. It combines the speed and consistency of traditional OCR with vision language model parsing for the documents that standard OCR consistently gets wrong. The result is high accuracy extraction on exactly the document types that have always been the hardest to process reliably.

It is built for:
→ Handwritten forms and notes
→ Historical documents and faded text
→ Documents with stamps, annotations, or overlapping content
→ Scans that state of the art OCR still misses

Agentic OCR is currently available to a select group of Hirize customers. If you are processing complex or non-standard documents and want to know whether it is the right fit for your use case, reach out to our team. We are always happy to take a look.

www.hirize.ai Hirize.AI

Most document AI follows rules, Agentic OCR thinks.
Swipe to see how document extraction is actually changing and what it means for anyone running documents at scale link in bio www.hirize.ai

Hirize has now processed over 500 million pages.

Fintech. HR tech. Healthcare. Global banks. Fortune 100s. AI teams.

Getting here taught us a lot about what it takes to run document infrastructure reliably at scale. http://hirize.ai

A common request we get: how do I share extraction results with my team?

Now you can. Add users to your team account with configurable expiration and org-level visibility controls.

Simple feature. Removes friction in a lot of workflows.

Accuracy is only part of the document AI challenge.
In production systems, meaning depends on layout. Tables, hierarchy, and proximity determine how values should be interpreted and verified.
Layout segmentation and bounding boxes preserve that structure—enabling citations, traceability, and review long after extraction.
We wrote a technical deep-dive on why layout needs to be treated as a first-class primitive in document AI, with examples from finance, healthcare, and legal workflows.