Today, I’m excited to announce that Hive has raised $85M in new capital, inclusive of a $50M Series D financing round at a $2B valuation and a previously unannounced $35M Series C financing round which closed last year at a $1B valuation. Our Series D round was led by Glynn Capital, with participation from our other major existing investors, including General Catalyst, Tomales Bay Capital, Bain & Company, and Jericho Capital.
This funding is an important milestone for the company, accelerating our ambition to be the leading provider of enterprise AI solutions to power the next wave of intelligent automation. Disruption triggered by the COVID-19 pandemic has pushed companies across industries to rethink how work is done, and this has resulted in an increased ambition and accelerated roadmap for the use of AI in enterprise automation. This capital infusion gives us the ability to meet the near-term market opportunity without constraints.
The use of AI in enterprise automation is still at its infancy. In recent years, companies across industries have embraced robotic process automation (RPA) to realize efficiencies from automating repetitive and lower-value tasks. A November 2020 Deloitte survey reported current use of RPA by 78% of companies and expected use of the technology by 94% of companies within the next three years. While RPA has delivered significant value across industries (and to the providers enabling these solutions), there is a ceiling on the types of activities that can be addressed with that technology‒generally limited to highly transactional activities such as application log in, data extraction, and form filling.
We aim to unlock the full potential of enterprise automation, spanning a set of more “intelligent” manual processes and new processes not feasible to scale with manual labor. Deep learning, the discipline of AI that we focus on at Hive, enables human-like interpretation of video, image, text, and audio‒newly enabling a next wave of intelligent automation. The market is ready. That same Deloitte study found that only 34% of companies report use of AI for automation today, but an incremental 52% of companies now plan to implement it over the next three years.
While the potential of AI models is endless, making them effective in a production setting is a separate matter altogether. We experienced this ourselves viscerally 4 years ago‒at the time as consumer app developers struggling to find out-of-the-box AI models accurate enough to handle processes such as content moderation. Forced to develop these models ourselves, we realized that other companies likely faced a similar dearth of high quality, publicly accessible models for common problems. From this challenge, the vision for Hive was born: performant, cloud-based and pre-trained AI models that are accessible via a simple API.
Over the past three and a half years, we have earned the trust of now more than 100 enterprise customers through an obsessive focus on accuracy‒rooted in pre-trained models that consistently and significantly outperform comparable solutions. The foundation of our model accuracy is predicated upon a belief that vast amounts of high quality training data is the most important factor of a performant model. We put such importance on this that we took the unusual step of building out our own distributed human workforce for the sole purpose of generating annotations for machine learning datasets at scale. Our Hive workforce has grown to be one of the largest in the world, with over 2.5M contributors and more than 4B human judgments generated. What began as a purely internal tool quickly became a valuable product in its own right, and today many of the world’s largest and most innovative companies rely on our platform to source and label raw data for developing their own AI models.
Being our own largest data annotation customer, we have the luxury of building AI models trained on unprecedented amounts of data. For instance, our visual content moderation model alone is trained on more than 600M judgments across 30+ classes; this is several orders of magnitude larger than any open source data set available. The resulting best-in-class model accuracy across our portfolio of solutions has driven significant growth in all our core company metrics. Over the past year, we’ve increased our customer base and revenue by more than 300% and are now processing billions of API calls a month. Since Q1 2018, we’ve increased API calls by nearly 60x:
What Makes Us Different
Our differentiation is in our industry-leading accuracy, enabled by the combination of our best-in-class ML technology with training data produced by our distributed labeling workforce of 2M+ contributors
Specifically, content moderation has been a key driver of our growth in the past year. This suite of models is now trusted by more than 75 customers globally, including content platforms such as Reddit, Yubo, Chatroulette, Omegle, Tango, and more, as well as leading dating sites, gaming platforms, verification services, and more. Our models provide real-time inference of video, image, text, and audio content, enabling clients to automatically identify and remove prohibited content across more than 40 classes, including sexual content, violence, gore, drugs, and hate speech. Companies that have integrated with Hive’s content moderation APIs have consistently increased the amount of content proactively reviewed and significantly reduced the level of human exposure to sensitive content, across both moderators and users.
Our portfolio of AI solutions for the media & entertainment industry has been another key driver of our recent growth, earning the trust of major media companies including NBCUniversal, large media agencies including Interpublic Group, and established brands including Walmart and Anheuser-Busch InBev. Our suite of AI models for the media space are collectively trained on more than 1B pieces of hand-labeled data and bring transformative new capabilities to areas such as contextual advertising and brand safety, advertising intelligence, measurement of sponsorship and branded content, and more.
Finally, we’re fortunate to have established a diverse set of marquee partnerships to accelerate our go-to-market on a global scale. Over the past year, we expanded our partnership with Bain & Company, which was also an investor in our Series C and Series D financing, to support a broader set of use cases across the firm’s practice areas. We partnered with Cognizant, one of the world’s leading professional services companies, to expand Cognizant’s use of automation across a diverse set of use cases across industries. And earlier this year, we announced a partnership with Comscore, a leading media measurement and analytics company, that will integrate Hive’s technology into Comscore’s product portfolio, including the launch of a reinvented branded content measurement solution enhanced with next-day, AI-powered data from Hive.
These partners have complemented our internal sales team in driving revenue for our business, but more importantly, they have also provided valuable guidance that has influenced our product roadmap and priorities. We look forward to announcing several other partnerships in the coming months.
Despite everything we’ve built over the past few years and all of the early commercial successes, we’re still scratching the surface of what’s possible. Like other major technology shifts in the past, AI will continue to permeate virtually all aspects of our lives, and all companies will have to adopt an AI strategy sooner than they would expect. While this additional capital gives us the resources to accelerate our growth to full potential, we will ultimately measure our success not by our funding figures‒or even our revenue‒but rather by the transformative impact that our clients’ products will have on the world.
If you’d like to join us on this mission, please check out our open roles here: thehive.ai/careers
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