AI document processing, built as an intake agent on your own systems.

Two kinds of company do. AI services firms, FactoryJet among them, design and build an agent on your own documents and systems, and you own the result. Product makers such as Rossum, Docsumo and Nanonets rent you a platform that you configure. Whichever you talk to, ask for the same things: a demo on your own documents, a written list of every system that will touch your data, and the name of the team that supports the agent after launch.

It depends on scope. Layer3 Labs' 2026 guide to US agency pricing puts a single-workflow build, with invoice intake as its example, at two to four weeks. One document type filed to one system sits at that end. Mixed packets, handwriting and several systems take longer. The pace is set by how soon a sample of real documents and a test login reach the builder. FactoryJet puts the timeline in writing with the fixed quote, and at first every document waits for a person.

It is software that takes an inbound document, such as an order, invoice, form, claim or application, and does the desk work a person would do with it. It works out what the document is, pulls the fields out, checks them against your records and files the result. The reading is done by an AI model, which can take a layout nobody set up in advance. The checking and the review around the model decide whether you can trust what gets filed.

IDP stands for intelligent document processing. It is the software industry name for reading documents with AI and passing the data to other systems. The term covers sorting documents by type, pulling out fields, checking them and sending exceptions to a person. ABBYY, UiPath and Hyperscience all use it for their platforms. On this page we say document intake, because the job starts when a document arrives and ends when it is filed.

It has two meanings, so check which one a seller has in mind. The first is producing documents, such as filling a contract or a letter from a template. The second is reading inbound documents and entering their data. Both showed up on page one of US Google results for the phrase on 11 Oct 2026. A NetDocuments page titled Legal Document Automation and Assembly Software sat near an Automation Anywhere page on processing documents at scale. This page is about the second meaning.

Five common ones. A distributor's order desk turns emailed purchase orders into sales orders. An accounts payable team turns vendor invoices into bills. An insurance agency files loss notices and photos to the right claim. A law firm sorts an intake packet into the matter. A property manager reads a rental application along with its pay stubs and ID. Each one follows the same seven steps, which are collect, sort, read, check, score, review and file.

An intake agent is software that handles the first contact with something new, whether that is a document, an enquiry or a client. A document intake agent reads what arrived, works out what it is and gets it to the right record. A client intake agent, common at law firms, asks a caller or a web visitor questions and opens the matter. This page covers documents. Our page on AI for law firms covers client intake.

OCR, short for optical character recognition, turns a picture of text into text. It tells you which words are on the page and nothing about what they mean. AI document processing adds the steps around it. Those are naming the document, finding the fields, checking them against your records, sending doubts to a person and filing the result. Microsoft describes its Read model as the OCR engine underneath its other document models, which shows where OCR sits.

The answer changes with your documents, which is why our list is grouped by kind of buyer and has no ranking. For one or two layouts with a spreadsheet at the end, look at Parseur or Docparser. For a team that wants a platform with a free start, look at Nanonets, Docsumo or Extend. For large volumes under a yearly contract, look at Rossum, ABBYY Vantage, UiPath or Hyperscience. Each entry in the list on this page restates the maker's own page as read on 11 Oct 2026.

Here are the list prices printed on the makers' own pages on 11 Oct 2026. Parseur starts at $49 a month for 100 pages and reaches $499 a month at 10,000, with larger plans above that and 20 free pages each month. Docparser runs from $39 to $159 a month, billed monthly. Nanonets is $100 a month for 100 credits after $50 in free credits. Extend's Scale plan is $500 a month. LandingAI's Team plans start from $250 a month. Rossum starts at $18,000 per year. The ABBYY, UiPath and Hyperscience pages we read print no price.

No. FactoryJet sells a service. Our team designs, builds and supports a document intake agent on your own documents and systems, and the finished agent belongs to you. There is nothing to subscribe to. The only new screen your people learn is the review queue made for them. If you would sooner have a product running this week, start with the ten named on this page, and ask us which we would try first.

When your documents come in one or two layouts, the data only has to reach a spreadsheet or a single app, and the review screen that ships with the product is enough. Speed is on the product's side too. Parseur, Nanonets, Docsumo and Extend each print a free allowance, so a trial costs an afternoon. Building makes sense once every field has to match your own records, documents arrive as mixed packets, or the result is filed in more than one system.

There is no neutral ranking. As a rough guide to who invests in search, Google's AI Overview for "ai document processing" on 11 Oct 2026 pointed to pages from Google Cloud, AWS, UiPath, Automation Anywhere, IBM, Databricks, Snowflake and LandingAI. A place in that box says little about fit. Shortlist by your document types and your monthly volume, then try two or three of them on your own files before you compare prices.

Yes, where the data arrives as documents or email. An AI model reads the page and fills the fields, and software enters them in your system. Two rules keep it safe. First, check each record against what your system already holds before it is saved. Second, send any field the AI is unsure of to a person. Typed text on a clean, familiar form is where AI does best. Handwriting and poor scans need more review.

For a single document, yes. You upload it and ask questions. For intake at volume, a chat window falls short in four ways. Someone has to upload each file. It has no login to your records, so it cannot check a policy number or an order. It keeps no log tied to those records. And nobody is told when it is unsure. Put the same model inside an agent that supplies those four things and it does the job well.

It depends on your documents, so we quote no single figure. Makers quote their own. ABBYY says its pre-trained skills deliver 90% accuracy at the start, and Hyperscience states accuracy rates of 99.5%. Neither number was measured on your files. What we do is measure. Ahead of go-live, documents your team has already processed are run through the agent, and we count field by field how often the two agree. Once live, any field below its bar is sent to a reviewer.

Yes, with limits. Amazon says Textract reads handwriting in English only. Microsoft says its Read model extracts print and handwritten text, and Google lists handwriting among the things Enterprise Document OCR detects. ABBYY says Vantage handles handwriting, barcodes and check boxes. Neat capitals in a form box read well. A scrawled note in a margin reads worse. In our builds, handwritten fields get a stricter bar, so more of them reach a reviewer.

Yes. Amazon's guidance is a scan of at least 150 DPI, and both Amazon and Microsoft put the smallest readable text at about 8 point at that sharpness. Google's OCR can return an image-quality score that flags eight defects, among them blur, glare, dark pages and cut-off text. The agent runs checks like these when a file arrives. A page too poor to read goes back to the sender or to a person. The agent does not guess at it.

Yes, and it is one of the harder parts. The agent finds where each document starts and ends, labels each one and keeps them linked as a case. The cloud tools do less here than their names suggest. Google says its splitters identify page boundaries but do not split the file for you, and Microsoft says its current custom classifier does not split by default. So the splitting step is built and tested on your own packets.

They go to a person, and only the doubtful part does. The reviewer sees the page, the field in question, the value the agent read and why it was flagged, such as a low score or a mismatch with your records. They correct or confirm it, and the document moves on. The correction is logged with their name and the time. No unsure field is filed until someone has looked at it.

Not in the old sense of building a template for each layout. We still need a sample of your real documents, for two reasons. It shows how well each field is read, and it lets us set the bar between sure and unsure. Where a classifier is trained to sort document types, Microsoft's minimum is five samples per type. The same sample becomes the test set that is run again whenever a model or one of your systems changes.

It depends on the provider and the plan, so check each one. These were read on 11 Oct 2026. OpenAI says data sent to its API has not been used to train its models since March 1, 2023, unless you opt in. Anthropic says it will not use inputs or outputs from its commercial products to train its models by default. Google says the same of its cloud AI platform unless you give permission. Amazon says Textract may use inputs to improve its services unless you opt out.

Three places. The original stays where it arrived, in your mailbox or your document store. A copy goes to the reading service or AI model that pulls the fields out. The finished record goes to your system of record. You get the list of every provider in that chain in writing before work starts, so your security lead can read each one's terms. The agent uses a login of its own, limited to the records it has to look up and create.

No. It reads, checks and files. It can lay out what the documents say and where they disagree, and it can open a task for the person who decides. The decision stays with that person, by name, and the log shows who made it. We draw that line on purpose. A reading mistake costs a correction. A wrong decision on a claim or an application costs far more, and it is yours to make.

Two published ranges give the scale. Layer3 Labs' 2026 guide puts a single-workflow build by a US agency at $5,000 to $15,000, and names invoice intake as an example. ProductCrafters' 2026 breakdown puts custom AI agents of all kinds between about $5,000 and more than $180,000. Where you land depends on how many document types the agent reads, how many systems it checks and files to, and how much handwriting is involved. FactoryJet gives you a fixed price in writing after a short scoping call.

We do. The build and the support that follows are each quoted as a fixed price, in writing, once a short scoping call is done. From go-live on, FactoryJet manages the servers, the AI models, the API connections and the maintenance, which keeps that load off your operations team. Part of that work is running your sample documents again every time a model or one of your systems changes, then fixing whatever the run shows.

Yes, and before any contract. Working software on your own documents is how we start. You pick a batch your team has already handled, an early build of the agent reads it, and you put the two sets of fields next to each other, unsure ones included. Should a rented product turn out to cover the job, you hear that from us then.

You do. The code, the written rules, the bars for each field and the test documents all belong to you, and you can have the agent run inside a cloud account that is yours. FactoryJet still looks after it day to day. Bring the work in-house later, or pass it to a different firm, and nothing is held back. The log of what the agent did goes along as well.

Any system that offers an API (a door for other software) or a supported import. FactoryJet has worked on Odoo, NetSuite, SAP Business One, ERPNext and custom ERPs, and we build Xero and HubSpot integrations. In scoping we check two things about your system. It has to let outside software create the record you need, and it helps if it offers a test copy to build against. Where a system offers neither, the agent prepares the record for a person to confirm.

Yes. Your team writes the rules with us, reviews the fields the agent is unsure of and makes every decision. For an agreed period at the start they see every document, so they learn where the agent is strong and where it needs a tighter bar. After that, their time goes to the exceptions and to the people on the other end of the documents, who are your customers, claimants, applicants and vendors.