Turn visual information into structured fields
Use the OCR engine to extract the information your workflow needs, including handwritten content. Define the target fields so the output can be reviewed and passed into another system.
READ THE DOCUMENT. UNDERSTAND THE EVIDENCE.
Document intelligence
From handwriting to answers.
Every detail has a place.
Extract structured information with our OCR engine, including handwritten text. Then ask complex questions across documents, tables, and illustrations with a RAG engine built for connected reasoning.
Discuss your use caseDOCUMENT INTELLIGENCE01 / 03
Structured OCR & handwritingTables and illustrationsMulti-hop RAG & web research
DOCUMENT INTELLIGENCE / OVERVIEW
Extraction and reasoning, in one document workflow.
A document may be a scanned form, a handwritten note, a report with tables, or a collection of linked references. Assisto combines document extraction with a retrieval-augmented generation engine so information can be captured as structured data and used to answer questions across a large collection.
What the engine
can do.
Capabilities that connect the model to the job you need it to perform.
Ask questions across a collection
Ingest document collections of varying types and volumes. The RAG engine retrieves relevant material and builds a grounded response, so users can ask a question instead of opening and searching files individually.
Read beyond running text
Tables, illustrations, and their surrounding explanations carry important meaning. The engine can use these elements to answer questions that depend on a row, a comparison, a figure, or a relationship described in the document.
Follow a question through multiple sources
Multi-hop reasoning connects evidence across documents. Where the question needs external context, web research can contribute additional sources alongside the ingested knowledge.
BUILT AROUND THE WORK
Where document intelligence fits.
Invoice and form processing
Extract references, line items, dates, and handwritten notes into structured records.
Technical due diligence
Compare information across reports, tables, and supporting illustrations to answer a multi-part question.
Enterprise research
Combine internal documents and web research to develop an answer with traceable evidence.
FROM CONFIGURATION TO USE
Make it work
for your business.
The deployment starts with your sources, your users, and the task the system needs to complete.
- 01Provide the documents and define any structured extraction fields.
- 02Ingest and index the collection for retrieval, including tables and visual material.
- 03Ask questions, inspect the supporting evidence, and refine the task.
- 04Connect structured output or grounded answers to the consuming business workflow.
A CLOSER LOOK
Common questions.
Can it read handwriting?
Yes. Handwritten text is part of the OCR capability. Representative samples help configure the fields and review requirements for the document types involved.
Can I ask questions about tables and illustrations?
Yes. The RAG engine can reason over tables and illustrations as well as text, connecting relevant information to answer complex questions.
Can it use more than one document for an answer?
Yes. Multi-hop reasoning supports questions that require evidence from multiple parts of the collection.
Can external research be included?
Yes. The engine can perform web research when the task needs information beyond the ingested documents.
YOUR NEXT CHAPTER
Let’s build something useful.
Bring your business challenge. We’ll bring AI, data, and a practical way forward.
