Unlocking AI Potential: Transform Enterprise Automation with Hypercell

Organizations today face immense pressure to modernize, cut costs, and enhance productivity. The key to unlocking successful digital transformation now includes mastering accurate data processing and harnessing the power of Generative AI to optimize the automation of back-office functions at scale. Discover how the latest enhancements in our core platform, the Hyperscience Hypercell, enable transformational AI at scale—leading to tangible ROI and competitive advantage in today’s rapidly changing and dynamic environment.

In this session, Hyperscience’s Lead Product Manager, Jamie Wittenberg, Director of Sales Engineering, Rich Mautino, and CTO, Brian Weiss show you how you can leverage the Hyperscience Hypercell to deepen automation capabilities and further embed Machine Learning into the core of your enterprise.

Watch the on-demand webinar to discover how Hyperscience can accelerate your digital transformation journey.

Transcript

Brian Weiss: Good morning everybody, and welcome to the Hyperscience R40 webinar. We do these on a biannual basis to keep you up to date on Hyperscience. There is a ton of really great work behind this release. We’ve got some innovations coming out they’ve been working on for a very long time, finally coming to fruition here, and we are really excited for it today.

By way of introductions, introducing myself, I am Brian Weiss, the CTO for the organization. I’m joined by Jamie Wittenberg, our lead product manager, and Rich Mautino, who is clearly auditioning for a James Bond movie with that headshot. Looking good, Rich.

By way of agenda today, the plan is: I want to give you a quick update on Hyperscience, our direction in the market, our investment priorities, and then we’ll pivot into some of the goodness in this new release. We have cherry picked a few highlights from the release. We can’t do it all. And so, look, I also see a bunch of familiar names on the call, on the attendee list, some from our customer advisory board. Hi everybody. It’s nice to see you again. But I also see many folks who might be new to Hyperscience. So what we’re gonna try and do is be both specific to the release on what’s coming in 40, but also generalize for those of you who might not be customers from learning about Hyperscience.

So kicking that off, we have just celebrated a 10 year anniversary. And to get started, I thought it would be useful to walk through the evolution of the technology, where we’ve invested and how it’s gotten us to where we are. Hyperscience was founded by machine learning experts and data scientists, two of them, who after selling their first machine learning-based venture to SoundCloud—it was a consumer use case—turned their attention to taking AI and deep learning to the core of the enterprise. And they went after the most challenging problems that are caused by messy, unstructured human data.

So a foundational investment for Hyperscience really is the models and machine learning that reads human data at scale. Our handwriting models are the gold standard in the industry today across multiple languages. Now, the other part of that same premise is that models don’t live alone. Like handing a customer a bag of models and having them go hire data sciences people and folks to figure out how to use ’em and run ’em is not gonna work because data scientists are gonna be scarce. So they invested heavily in the premise that it should be supported by a platform which is usable by business users, not data science folks. So they invested in a low-code model management platform which enables business users, not data scientists, to quickly train up sovereign models that are built on their own enterprise data.

Secondly, Hyperscience pioneered a very unique approach to just-in-time human in the loop for AI and automation, whereby accuracy is a brokered target inside the model. So human users get called in by the AI or the digital worker to unstick the specific points of confusion during processing. Now, instead of the old school way of doing this—you punt it to a black box and it spits out a bunch of data with no accountability to accuracy, so it might be incorrect, that leaves you to have to go create a QC harness—Hyperscience changes that and takes responsibility for a hundred percent of the work, right, at human level accuracy. So what you end up doing is you take a fraction of your spend on your BPO or whatever is taking you to make it right with people, and you put those people right next to the machine at the point of processing.

And the third premise here that the founders started and we have continued to invest in is this is turnkey infrastructure. Again, it’s not a bag of models that you work with people, right? They invested in making it cost-effective and secure. So you don’t need to overspend on GPU if you can get a 99% accuracy model, narrower model on CPU. Why do that? Right? So Hyperscience can easily be deployed and run in any environment. We run in air-gapped environments right now, on-prem, Cloud, SaaS, at the highest level of security and controls for your data.

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Jamie Wittenberg: Thanks, Brian. I certainly will. So let me make sure I understand what you’re saying. So enterprises have mountains of unstructured content across a variety of sources, including paper and digital documents, and they need to get that data out of that unstructured content and structured content as well into business systems and systems of record in standardized formats at a high accuracy rate with very little human intervention without devoting entire engineering and data science teams to that. Is that what you’re telling me?

Brian Weiss: Nailed it. Exactly.

Jamie Wittenberg: Well, with the latest version of the Hyperscience Hypercell, that is no problem. So bringing AI to the enterprise for automation is not simple, right? For starters, one popular model repository has over 1 million models available. How do you even choose the right one for the particular job that you wanna do? Whether that’s classifying documents, extracting tabular data, extracting unstructured data, transcribing data.

So there’s a lot to be done just to get a single AI automation use case running and Hyperscience’s entire mission is to make that a turnkey operation for the enterprise. This is what we’ve been doing for 10 years, and with V40, it just gets better.