Caddi vs. ABBYYCaddi reads documents without templates, so a new document type doesn't wait on configuration.
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.
- 99%of agent runs complete successfully
- ~45 minon average to build your first agent
- 1M+back-office tasks completed
Jason AlafganiUpdated Sep 23, 2026
Clean fields still need someone to file them.
The data comes out accurate, then a person keys it into the matter system, updates the CRM and sends the follow-up.
High-accuracy extraction from known document types, handed to a downstream system to finish.
Caddi reads the document and finishes the workflow it lives in: intake, decisions and writing to the system of record over APIs.
A new document type means a new template.
Forms change and formats drift, and every change goes back to whoever builds and tunes the templates.
Templates and trained ML per document type, built by ABBYY partners or IT and retuned as formats drift.
Caddi reads varied PDFs and shared inboxes without templates or training, so a new form doesn't wait on configuration.
Ops owns the process but not the setup.
When the people who run the work can't change how it's automated, every adjustment becomes a request to someone else.
Configured and maintained by a team that sets up templates and tunes OCR.
A paralegal or billing coordinator describes the workflow in chat or on a screenshare. Known steps run as code, the same way every time, and Caddi maintains it.
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.
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 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.