Get to Know the Hyperscience Hypercell

Get to Know the Hyperscience Hypercell

Discover the Hyperscience Hypercell, an all-in-one solution designed to meet the most complex document automation needs of today’s enterprise.

In this session, Brian Weiss, CTO, and Chris Bloomfield, Senior Director of Sales Engineering will show you how to:

Watch the on-demand webinar & demo to witness a fundamental shift in document-centric automation that can revolutionize your operations.

Transcript

Brian Weiss: Good morning, everybody. Welcome to the webinar where we are going to introduce you to the Hyperscience Hypercell.

I am joined by my colleague Chris Bloomfield, Senior Director of Sales Engineering, and I’m Brian Weiss, the CTO. Chris, I realize we’re doing a “follow the sun” model here. Chris is based in London. I’m based in San Francisco, so we’re getting a little global support model going here.

What we’re going to do today is introduce you to the Hypercell and Release 39. But at the same time, look, I realize many of you who are customers maybe are not up to date in the latest two or three. So we want to give you a feel and a sense for who we are now, where we’re going, the types of investments we’re making, where we sit in the industry, and the types of disruption that we’re driving, as well as some of the details of the platform itself.

Let’s get into it just to level set. Hyperscience is a technology company founded roughly eight years ago by machine learning engineers. They determined that if you could bring a machine learning proposition to the hard problems in the enterprise to bring AI into the enterprise, meaning: Can I get what humans have to do? Can I train and build machines to understand that information and take care of those tasks? That is fundamentally an AI proposition.

They went after handwriting and some of the most complex human information that comes into the enterprise as their first endeavor. That has really grown now to a company that brings AI into the enterprise in very sensitive information. We operate as both a software platform as well as SaaS, as well as a hybrid cloud. We operate by the end of this year in FedRAMP High in the federal government space. We are well capitalized, funded roughly $300 million since origins by top tier investors. We are roughly 200 and some odd employees. More than half of those are engineers. And half again of those are machine learning engineers.

The company has grown up in heavily regulated verticals. What that means is we are dealing with very sensitive data. You can see the government industries here, not just social security numbers but also insurance industries where building machine models with those digital workers that do that work has really hardened the platform to enterprise class.

What we achieve in the market is hyperautomation, and we define hyperautomation as automating tasks at human level accuracy. We regularly get 99% accuracy and 98% automation. The beautiful thing about the platform is it is designed to create accuracy. It takes a human-in-the-loop approach.

The other piece is, many of you probably think these days that AI, while exciting, also acts like a solution looking for a problem. Most of our customers are excited about ROI. How do I take what AI can do for me and deliver immediate results? The way that we do that is by inserting a digital worker effectively to anything that’s a human task.

A couple of examples: We work with IBM at the VA and have processed roughly a billion pages a year, looking at $400 million of savings over time. What’s important about this is not just the savings, but for us and for veterans, it now takes three weeks instead of three months to process a claim.

Chris Bloomfield: Great, thanks Brian, and pleasure to be here and speaking to you all today. In our latest release, R39, we aim to drive a fundamental shift in AI-driven automation. This focuses on three core areas: building new world-class extraction and classification models, the ability to train, QA, and supervise those in real-world applications with our no code Hypercell approach, and a flexible orchestration layer to enable end-to-end automation.

Moving on, let's discuss model lifecycle management. The first of which is Incremental Training. This enables you to take an existing model, add data as this representative data set changes on your day-to-day operations. The next one is Training Data Management.

The next one is around Automated Upgrade Management. Ensuring that your infrastructure is resilient to upgrades, that you are able to seamlessly move workflows and models and keep up to pace with the Hyperscience platform. The next one, Trainer Resiliency, picks up where the trainer left off if a trainer stops due to network outages. The last one is Audit Log capability, tracking user events and processes metadata.

The second half of this session discusses our approach to delivering World Class Enterprise Hyperautomation. This includes automation elements around Freeform Text Fields and Multiple Tables Automation. We’ve focused on improving our processing for structured unstructured human-friendly content, particularly around Chinese.

In the concluding segment, Brian and Chris emphasize the advantages of an ensemble of using Hyperscience’s technology along with LLMs, illustrating specific customer case studies. They stress that AI's accuracy really matters in processing sensitive tasks, where getting it right is critical to operations. They urge to analyze the applicability of AI in different business processes, and underscore the capabilities of the Hyperscience platform in driving enhanced automation and decision-making processes.