Read and Repair

Machine Learning Delivered


The READ Engine

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.


What This Means For Your Business

Generate powerful throughput, speed, and accuracy

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.

Leverage fully automated digitization

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%.

Meet the strategic needs of your business

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.

Paper remains, can you innovate?

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.

Ensure secure and scalable workflows

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.


FAQ

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.


 

The REPAIR Engine

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.


What This Means For Your Business

Reduce cycle times by speeding up processing even on the most difficult of data

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%.

Achieve better data quality and improve the AI

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.  

Meet the strategic needs of your business

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.

Analyze trends and fix inefficiencies

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.

Improve customer and the operations experience

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.


 FAQ

What fields are “flagged” for the repair interface?

  • REPAIR flags particular fields based on specified workflows, validation rules, transformation needs, enrichment rules, and data that needs to be repaired to ensure data is ready to be routed to downstream systems.

Why do you need to ‘repair’ data?

  • Data that needs to be repaired have low confidence scores or are unable to be digitized and marked as “IMPOSSIBLE.” With Captricity READ, you can control the review rate to achieve your desired accuracy levels.

What are “validation” fields?

  • If certain fields don’t meet set criteria, then the field can’t be processed and is flagged. Criteria can be customized. For example, social security #’s have to be in nine digits.

What are “transformation” fields?

  • To prepare data for downstream delivery, some fields may need to be standardized. For example, transformations include changing the date formatting, changing case, find and replacing values, stripping out punctuation and having numbers, and other custom workflows.

What are “enriched” fields?

  • Fields that are enriched are integrated with third-party systems, like SmartyStreets for out-of-the-box data additions or enrichments. Enrichments could include adding addresses, phone numbers, etc.

How much faster does fixing data take with the interface than traditional methods?

  • By our estimates, reviewing the data in REPAIR’s intuitive interface can reduce the amount of human effort it takes by 80%.

Digitize Thousands of Documents With Ease.

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