With Captricity READ, businesses can reimagine how they process and think about paper to digital workflows. Trained on millions of real-world examples using language and visual machine learning models, Captricity’s READ engine outperforms humans at data capture—regardless if it’s typed or handwritten. Captricity enables paper to move at the speed of digital, and accelerates how businesses is done.
The READ engine processes hundreds of inputs at the same time and is 1000 times faster than humans at processing information. Data can be returned in minutes, and to digital workforces to enable straight-through processing. Captricity was created in the real-world, solving enterprise operational problems with paper to digital processing, and doing it in a whole new way.
Fully automating your paper to digital strategy dramatically increases your straight through processing abilities. Many of our customers have reduced operational costs by 80%, while increasing their throughput by 300%.
Many organizations are moving forward with digital workforces through the use of Robotic Process Automation (RPA). Captricity works alongside these digital workforces and opens up many workflows that are closed to automation without a paper to digital solution.
With poor digital adoption rates and the difficulty for some demographics to move off paper, sometimes customers have realized that the paper problem will remain. Improve the customer experience and innovate strategically in a way that makes sense to your business.
Captricity is a secure, cloud-based solution that enables organizations to transform their paper to digital process at scale. The cloud enables the system to scale from very small to extremely large in a matter of minutes. Combined with our unique cloud security models, you won’t find a better way to transform your paper to digital journey.
Isn’t this just OCR?
No, Captricity is different – we’ve used computer vision and machine learning to build a self-learning system for any form. Self-learning re-defines how forms can be processed and accelerates implementation and delivery. Machine learning means that the system gets smarter.
Do you process handwriting?
Yes, Captricity is proven to process handwritten forms at up to 99% accuracy.
Is it on-premise or cloud based?
Captricity is cloud-based, hosted in a secure Amazon Web Services environment.
We have a lot of documents. Can it scale?
Captricity has scaled to millions of documents per hour for enterprise-wide processing abilities. We can currently handle over 1 million documents per hour.
How fast is it?
Documents can be returned in less than fifteen minutes for verification.
Captricity REPAIR is a flexible solution that accelerates the time to review digitized data. With REPAIR, businesses improve data quality over time and handle exceptions on the most difficult of workflows.
Captricity speeds up the time it takes to process your workflows with a solution for end-to-end automation. The platform can improve performance over 300%, and reduce operational costs by as much as 80%.
Good data validation drives the highest data quality levels. Through the REPAIR interface, humans validate data while improving the training of the core machine learning engine.
The REPAIR engine gives you the ability to optimize across speed, accuracy, and cost to meet the unique needs of your business. If you need to validate critical fields that need 99% accuracy, you can do it with Captricity. If you need better than human accuracy while ensuring lightning fast turnaround time, you can do it with Captricity.
Collect data on changes the customer is making. Know which fields are yielding the most problems and leverage the data for operational changes to forms and workflows.
REPAIR’s intuitive experience enables better data quality, increases straight-through processing and gives internal data teams an easier way to expedite the exception-handling process.
What fields are “flagged” for the repair interface?
Why do you need to ‘repair’ data?
What are “validation” fields?
What are “transformation” fields?
What are “enriched” fields?
How much faster does fixing data take with the interface than traditional methods?