Intelligent document processing captures and extracts structured data from documents at scale. Tools like Tungsten (Kofax), Hyperscience, and ABBYY do this well, using OCR, templates, and ML models you train on samples. They nail the extraction step, and then they stop at "structured data out." A human or a downstream system has to move that data onward and finish the actual work, and setup means labeling fields or training models that you maintain as formats drift.
The 2026 version expands from the document step to the whole workflow the document lives in. AI reads varied documents natively, with no per-field templates and no model training, set up by recording the task once. It makes validated decisions, and it writes to the systems of record, the DMS, CRM, and custodians, automatically. Built and maintained for you. Document processing becomes workflow automation that happens to start with a document. Caddi is what IDP becomes when it stops at extraction no longer.
The basics
What is Traditional IDP?
Intelligent document processing: capturing and extracting structured data from documents using OCR, templates, and trained ML models, typically set up by labeling fields or training on samples and maintained as formats change.
What is Caddi?
The deterministic AI automation platform for ops and admin teams. Ops teams teach Caddi their workflows over a screen share, and then Caddi runs them reliably hundreds of times a week.
What's changing in 2026
Traditional IDP is organized around capture and extraction: OCR the document, recognize the layout, pull the fields, output structured data. To make that accurate you label fields per template or train ML models on samples, and you re-train or re-label as formats drift. It's powerful at the extraction step, but the tool's job ends at structured data out, the surrounding work still falls to a person or another system.
In 2026 the frame widens past extraction. AI reads varied documents natively, so setup is a recording rather than a labeling or model-training project. And because the real job is rarely "just extract the data," the automation makes validated decisions and writes the results into your systems of record automatically. Intelligent document processing becomes workflow automation that happens to start with a document.
Intelligent document processing 1.0 → 2.0
Four shifts turn document capture into automation for the whole workflow the document lives in.
From extraction to the whole workflow
Traditional IDP owns one step: structured data out of a document. But extraction is rarely the job, the job is the intake completed, the record updated, the matter advanced. The 2026 version covers the whole workflow the document lives in, so the value isn't "fields extracted" but "the work done," with the document as the first step rather than the finish line.
From templates and training to recording
Classic IDP needs per-template field labeling or ML models trained on samples, and re-training or re-labeling when formats drift. The 2026 model reads varied documents natively from a recorded example, so there are no templates to maintain and no models to train, and far less maintenance as documents change.
From data out to validated decisions and writes
Extracting data is only useful if something acts on it. Traditional IDP stops at structured data; a person or downstream system decides and moves it onward. The 2026 version makes validated AI decisions on what it reads and writes the results into your DMS, CRM, and custodians automatically, so the data lands where work happens without a human in the middle.
From capture silo to multi-integration
Classic document processing is a silo: document in, structured data out. The 2026 version is multi-integration by design, reading from and writing to the systems of record across your stack (70+ tools), so extraction is one step inside an automation that completes the process rather than a point solution bolted onto everything else.
The old way vs. the 2026 way, at a glance
| Traditional IDP | Caddi | |
|---|---|---|
| Scope | Extraction step only | The whole workflow around it |
| Setup | Label fields or train models | Record the task once |
| Output | Structured data out | Decisions made and data written |
| Document variety | Per-template; re-train on drift | Varied documents, read natively |
| Integrations | Capture silo, data handed off | DMS, CRM, custodians (70+ tools) |
| Maintenance | Re-train / re-label as formats drift | Maintained for you |
How they score where it counts
Which fits your situation?
Both models have a place. Tap the scenario closest to yours to see which approach wins — and why.
Which fits your situation?
Traditional IDP
When the job is purely extraction from a known set of documents at high volume and a downstream system takes it from there, a mature IDP tool does that one step well.
Beyond extraction, to the whole workflow
See Caddi build a workflow from a screen recording and run it across 70+ tools. Explore real examples, compare Caddi to the tools you know on the comparison hub, or book a demo.
Do more with less
See Caddi in action
Tell us where to reach you and the calendar opens right here. In 30 minutes we'll show you how Caddi automates the back-office work that grows with your clients—built, run, and maintained for you.
Frequently asked questions
How is intelligent document processing changing in 2026?
It's expanding from the extraction step to the whole workflow the document lives in. The 2026 model reads varied documents natively with no per-field templates and no model training (setup by recording), makes validated decisions, and writes the results into your DMS, CRM, and custodians automatically, all built and maintained for you.
What's the difference between IDP and document automation?
Intelligent document processing focuses on capturing and extracting structured data from documents via OCR, templates, and trained models, stopping at structured data out. The 2026 approach treats the document as one step in a larger workflow, making decisions and writing to systems of record, so it automates the work rather than just the extraction.
Do I still need to train models or label templates for document processing?
Not in the 2026 model. Traditional IDP needs per-template field labeling or ML models trained on samples, with re-training as formats drift. The modern approach reads varied documents natively from a recorded example, so there are no templates to maintain and no models to train, which cuts setup time and ongoing maintenance.
Can intelligent document processing finish the work, not just extract data?
Yes, when it's built as workflow automation. Instead of stopping at structured data out and handing off to a person or downstream system, the 2026 model makes validated decisions on what it reads and writes the results into your systems of record automatically, so the document step flows straight into the work getting done.