While working at Hugging Face, engineers Mark McQuade and Brian Benedict ran into challenges helping enterprise customers adopt GenAI. Some companies didn’t want to use closed-source AI APIs due to the perceived lack of transparency — but also resisted open source models over security concerns.
“We came to realize that the primary challenge was overcoming the trust deficit in existing generative AI systems,” McQuade told TechCrunch in an email interview, “particularly regarding performance and security.”
McQuade, who’d also done stints at Rackspace and computer vision startup Roboflow, was inspired to search elsewhere for solutions. After failing to find any, he — along with Roboflow’s head of machine learning Jacob Salowetz and Benedict — developed a platform from the ground up to let organizations build and train GenAI models within a secure compute environment.
Launched last February as Arcee, the platform — maintained by its Miami-based namesake startup — has attracted $5.5 million in venture funding to date from investors including Long Journey Ventures, Flybridge, Centre Street Partners, Wndrco, 35V, AIN Ventures and Hugging Face CEO and co-founder Clément Delangue.
“Arcee revolutionizes AI for highly regulated industries such as legal, healthcare, insurance and financial services,” McQuade said. “Arcee’s platform enables these sectors, as well as all organizations with highly proprietary data, to build specialized language models using their own data securely within their own cloud environment.”
As the GenAI boom continues, a number of startups have emerged to tackle the problem that McQuade describes: training private enterprise models securely and efficiently.
Contextual AI, for example, offers tools to tailor GenAI models — specifically large language models (LLMs) along the lines of OpenAI’s ChatGPT — to business use cases. Giga ML delivers tooling to help companies deploy LLMs offline. There’a also Reka, which builds custom models for corporate applications, such as document analyses.
So how does Arcee differentiate itself? In a few key ways, McQuade claims.
First, Arcee’s platform is end-to-end, employing an “adaptive” system for training, deploying and monitoring GenAI models. It also operates in a virtual private cloud, offering what McQuade describes as “superior” fine-tuning and security to mitigate privacy risks.
A rough schematic of Acree’s platform, which trains, deploys and maintains GenAI models within a customer’s chosen environment.
The emphasis on security is probably wise, considering surveys show it’s a top issue for enterprises. According to a recent Salesforce poll, 71% of IT leaders expect that generative AI will introduce new security risks to data.
“Arcee’s approach allows organizations to build and train these models within their own secure environments,” McQuade said. “This not only ensures data privacy but also grants businesses full ownership of their AI models and technology stack.”
Now, assuming it’s true that Arcee’s platform is indeed better than the competition’s in some respects, Arcee still has a long road ahead of it to establishing a foothold in the increasingly crowded market for GenAI dev platforms. It’s not just startups it has to contend with. Incumbents like Google, Microsoft and Amazon are competing in the space, too — see Vertex AI for example.
Arcee’s initial backers have faith, however. Here’s Flybridge’s Jesse Middleton:
“Our decision to invest in Arcee was driven by three compelling factors,” he said via email. “Firstly, analysts estimate that 2.5% of all enterprise software spending today is on AI applications. This booming AI market, particularly in industry-specific solutions, positions Arcee uniquely as a standout player. Secondly, the team’s expertise and early success landing multiple Global 2000 customers demonstrate a profound understanding of market demands. Lastly, the current AI landscape demands innovative solutions like Arcee’s, making this the opportune time to invest in the future of AI and small language models within every department of these enterprises.”
But once again: is the demand for GenAI in the enterprise high enough to support yet another platform? It’s a legitimate concern, I think.
In a recent Boston Consulting Group of over 1,400 C-suite executives, only about half of the respondents said that they expect GenAI to bring substantial productivity gains to the workforces that the oversee. Another poll by BCG found that more than half of exec decision-makers were “discouraging” GenAI adoption over worries that it’d encourage bad or illegal decision-making and compromise their employer’s data security.
As you might expect, McQuade is of the strong opinion that Arcee can stand out — and excel, even — with the right customer and investor support. He says that the capital raised so far will enable Arcee to expand its workforce while building out the platform and growing into new markets.
“Our clients’ enthusiastic reception has shown us that we’re not just filling a gap in the market but actually leading the way in AI innovation,” McQuade added — while declining to name those clients. “We’re committed to building value for our investors, partners and customers, and believe that the current focus should remain on our technological and market advancements rather than on specific financial metrics.”
It’s not naive optimism on McQuade’s part necessarily — at least concerning the investment piece. According to Pitchbook, VCs poured $21.4 billion into GenAI startups last year through September, up from $5.1 billion in 2022. Assuming 2024 is more of the same, the dollars should flow relatively freely.
