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Comparison

Caddi vs. ABBYY: Which Should You Use?

A direct comparison of Caddi and ABBYY: template- and ML-based document intelligence vs. whole-workflow automation, how each is set up and maintained, and which fits regulated back-office teams in law and finance.

Caddi and ABBYY both read documents, but they stop at very different places. ABBYY is document intelligence with deep OCR heritage, part of intelligent document processing: products like FlexiCapture and Vantage extract structured data, then hand it off. Caddi reads the document and finishes the workflow it lives in. ABBYY suits high-accuracy extraction from known document types; Caddi suits ops teams in law and finance that want the whole job done, live in days.

The basics

What is ABBYY?

A document intelligence platform with deep OCR heritage. Products like FlexiCapture and Vantage extract structured data using templates and trained ML, built for high-accuracy extraction from known document types.

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.

The fundamental difference

ABBYY extracts: it uses templates and trained ML to turn a document into structured fields and then stops at "structured data out", leaving a person or a downstream system to move that data onward and finish the work. Standing it up means building templates for each document type and training ML, then maintaining them as formats drift. Caddi reads varied PDFs and inboxes natively, with no per-field templates and no model training, and then automates the whole workflow the document lives in, intake, decisions, and writing to the system of record, set up by recording the task once.

What it takes to stand one up

The extraction-silo lifecycle and the Caddi lifecycle look nothing alike. Toggle between the two to compare how each is built, how it runs, and where the work actually finishes.

Ops staff record it; Caddi finishes the work
  1. 1
    Record the task on a screen-shareA non-technical teammate walks through the workflow once, no templates, no training.
  2. 2
    Caddi reads documents nativelyVaried PDFs and shared inboxes are handled out of the box, no per-field templates.
  3. 3
    Caddi writes deterministic code over APIsIt runs the whole workflow: intake, decisions, and writing to the system of record.
  4. 4
    Caddi maintains itUpkeep and edge cases are handled for you, often with automations live in days.
Tap a tab to switch between the template/train/extract/hand-off lifecycle and the Caddi record-once model.

Caddi vs. ABBYY at a glance

ABBYYCaddi
CategoryIntelligent document processing (capture/IDP)Whole-workflow automation
What it deliversStructured data outThe finished workflow
How it's set upBuild templates, train MLRecord the task once on a screen-share
Document handlingOCR, templates, trained MLNative reading of varied PDFs & inboxes
After extractionHuman or downstream system finishes itCaddi finishes it over APIs
Who owns itCapture / OCR specialistsNon-technical ops staff
MaintenanceRetune templates as formats driftBuilt & maintained by Caddi
Best fitHigh-accuracy extraction, known typesLaw & finance back office

How they score where it counts

ABBYY is a mature document intelligence platform with deep OCR heritage and strong extraction accuracy on known document types. Caddi trades some of that template-tuned accuracy for finishing the whole workflow, native document handling with no training, and a done-for-you model built for regulated ops.

CaddiABBYY
Extraction accuracy, known typesFinishes the workflowSetup without templatesDone-for-youTime to liveLow maintenance
Directional scoring (out of 5). ABBYY leads on template-tuned extraction accuracy; Caddi leads on finishing the workflow, setup without templates, speed, and being maintained for you.
Illustrative effort to launch & sustain one workflow
ABBYY: template, train + maintainHigh
Caddi: record + maintained for youLow
Directional. ABBYY effort is dominated by building templates, tuning extraction, and retuning as formats drift; Caddi shifts setup to a recording and upkeep to the vendor.

When ABBYY is the right call

ABBYY is a strong fit when the job is purely high-accuracy extraction from known document types, you have the team to build templates and tune OCR and ML, and the extracted data feeds an existing downstream system that already finishes the work.

When Caddi is the right call

Caddi is the better fit if the people who own the process are non-technical, if your highest-value work is document- and inbox-heavy and needs to be finished, not just extracted (intake, filing, PDF → system of record, triage), if you want it set up by recording once instead of building templates and training ML, and if you need automations live in days with SOC 2 compliance and audit trails built in.

Which fits your situation?

Best fit

Caddi

Caddi reads the document and runs the rest of the workflow over APIs, so the work actually gets done.

Many teams come to Caddi because extracting data was never the goal, finishing the work was. If ABBYY gets you accurate fields but a person still has to move them onward, Caddi reads the document and automates the whole workflow your ops team can own, with no templates to build.

See Caddi next to your ABBYY workflows

Bring a document workflow you run through ABBYY today. Caddi will build it from a screen recording, read the documents natively with no templates, and run it across 70+ tools. See real examples or book a demo. For the broader landscape, see document automation software.

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

What is the difference between Caddi and ABBYY?

ABBYY is document intelligence with deep OCR heritage: products like FlexiCapture and Vantage extract structured data using templates and trained ML, then hand it off to an existing downstream system. It stops at structured data out. Caddi reads documents natively, with no per-field templates or model training, and automates the whole workflow the document lives in. ABBYY is built for high-accuracy extraction from known document types; Caddi is built for non-technical ops teams in law and finance.

Is Caddi a good ABBYY alternative?

Yes, especially when the goal is to finish the work the document triggers rather than just extract its fields. ABBYY gets you accurate structured data out, then a downstream system or person finishes the job. Caddi reads varied PDFs and inboxes natively with no templates, then runs the rest of the workflow over APIs, set up by recording the task once.

Does Caddi require templates and OCR tuning like ABBYY?

No. ABBYY's accuracy depends on configuring templates and training ML for each document type, then maintaining them as formats drift. With Caddi, a non-technical teammate records the workflow once and Caddi reads documents natively, so there are no templates to build and no models to train.

When should I use ABBYY instead of Caddi?

ABBYY can be the right choice when the job is purely high-accuracy extraction from known document types feeding an existing downstream system, and you have the team to configure templates and tune OCR. Caddi is the better fit when a non-technical team needs the whole workflow automated, not just the extraction step.