Caddi and Hyperscience both read documents, but they stop at very different places. Hyperscience is ML-model-based intelligent document processing: it extracts structured data with trained models, then hands it off. Caddi reads the document and finishes the workflow it lives in. Hyperscience suits orgs with data-science capacity and huge document volumes; Caddi suits ops teams in law and finance that want the whole job done, live in days.
The basics
What is Hyperscience?
An ML-model-based document processing platform that extracts structured data using trained machine-learning models. It is built for enterprise-grade extraction accuracy and needs training data and ongoing model management.
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
Hyperscience extracts: it uses trained models 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 collecting training data, training models on sample documents, and managing those models as formats drift. Caddi reads varied PDFs and inboxes natively, with 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-engine 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.
- 1Record the task on a screen-shareA non-technical teammate walks through the workflow once, no training data, no models.
- 2Caddi reads documents nativelyVaried PDFs and shared inboxes are handled out of the box, no model training.
- 3Caddi writes deterministic code over APIsIt runs the whole workflow: intake, decisions, and writing to the system of record.
- 4Caddi maintains itUpkeep and edge cases are handled for you, often with automations live in days.
Caddi vs. Hyperscience at a glance
| Hyperscience | Caddi | |
|---|---|---|
| Category | Intelligent document processing (ML extraction) | Whole-workflow automation |
| What it delivers | Structured data out | The finished workflow |
| How it's set up | Collect data, train models | Record the task once on a screen-share |
| Document handling | Trained ML models | Native reading of varied PDFs & inboxes |
| After extraction | Human or downstream system finishes it | Caddi finishes it over APIs |
| Who owns it | Data-science / ML specialists | Non-technical ops staff |
| Maintenance | Ongoing model management | Built & maintained by Caddi |
| Best fit | Huge volumes of similar documents | Law & finance back office |
How they score where it counts
Hyperscience is a strong, ML-based extraction engine built for huge volumes of similar documents. Caddi trades a tunable extraction model for finishing the whole workflow, native document handling with no training, and a done-for-you model built for regulated ops.
When Hyperscience is the right call
Hyperscience is a strong fit if you have data-science capacity, process huge volumes of similar documents, want a tunable ML-based extraction engine you can train for accuracy, and feed the extracted data into existing systems that already finish 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 collecting training data and managing models, and if you need automations live in days with SOC 2 compliance and audit trails built in.
Which fits your situation?
Caddi
Caddi reads the document and runs the rest of the workflow over APIs, so the work actually gets done.
See Caddi next to your Hyperscience workflows
Bring a document workflow you run through Hyperscience today. Caddi will build it from a screen recording, read the documents natively with no training, 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 Hyperscience?
Hyperscience is ML-model-based intelligent document processing: it extracts structured data from documents using trained machine-learning models, which need training data and ongoing model management. It stops at structured data out, then a person or downstream system finishes the work. Caddi reads documents natively, with no model training, and automates the whole workflow the document lives in. Hyperscience is built for orgs with data-science capacity processing huge volumes of similar documents; Caddi is built for non-technical ops teams in law and finance.
Is Caddi a good Hyperscience alternative?
Yes, especially when you do not have a data-science team and need the work finished rather than a tunable extraction engine. Hyperscience gives you accurate extraction once models are trained and managed. Caddi reads varied PDFs and inboxes natively with no training, then runs the rest of the workflow over APIs, set up by recording the task once.
Does Caddi need training data and model management like Hyperscience?
No. Hyperscience depends on training data and ongoing model management to keep extraction accurate. With Caddi, a non-technical teammate records the workflow once and Caddi reads documents natively, so there is no training data to collect and no models to manage.
When should I use Hyperscience instead of Caddi?
Hyperscience can be the right choice for an organization with data-science capacity that processes huge volumes of similar documents and wants a tunable, ML-based extraction engine feeding existing systems. Caddi is the better fit when a non-technical team needs the whole workflow automated, not just an extraction engine to tune.