Caddi vs. HyperscienceCaddi reads new document types without training a model, then finishes the work in your systems.
A direct comparison of Caddi and Hyperscience: ML-model-based document extraction 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
Training data comes before any time is saved.
Gathering and labeling sample documents is a project of its own, and it happens before a single field is extracted.
Trained ML models tuned for enterprise-grade accuracy, deployed and tuned with Hyperscience.
Caddi reads varied PDFs and inboxes with no model training. About 45 minutes on average to the first agent.
Extraction is one step of the job.
Once the fields are out, someone still opens the account, files the documents and chases what's missing.
Outputs structured data to the systems that finish the work downstream.
Caddi carries on from the document: it updates the CRM, files to the system of record and sends the follow-up, over APIs.
Models need a team to stay accurate.
Formats drift, and retraining lands on data-science capacity that most firms keep for other work.
Your IT team owns the models and flows, retraining as formats change.
Caddi maintains the automation for you, and every step of every run is logged so ops can review the work.
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.
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 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.