Arcee AI raises $24M Series A led by Emergence Capital to bring small language models to enterprises. Secure, efficient AI solutions for regulated industries.
It all began with a problem that needed solving. Mark McQuade, Brian Benedict, and Jacob Salowetz, three engineers who had worked at companies including machine learning innovator Hugging Face and computer vision pioneer Roboflow, saw firsthand the challenges enterprises faced with generative AI (GenAI). Companies balked at closed-source AI due to transparency concerns and shied away from open-source models over security issues. The trio realized that the core issue was a trust deficit in existing GenAI systems, particularly regarding performance and security.
This insight led to the birth of Arcee AI. Launched in Miami last February, Arcee quickly became a first port of call for enterprises seeking secure, efficient GenAI solutions. Its platform allows companies to build and train GenAI models within a secure environment. “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 told TechCrunch.
Arcee’s unique approach quickly caught the attention of investors. After raising $5.5 million in seed funding from notable names like Long Journey Ventures, Flybridge, and Hugging Face’s CEO Clément Delangue, the momentum didn’t stop.
Just six months later, Arcee announced a $24 million Series A funding round led by Emergence Capital. The new funding will fuel their mission to bring small language models (SLMs) to the masses, alongside the launch of their new hosted SaaS version, Arcee Cloud.
What sets Arcee apart? According to McQuade, it’s the company’s end-to-end platform that operates in a virtual private cloud, providing superior fine-tuning and security: “Arcee revolutionizes AI for highly regulated industries such as legal, healthcare, insurance and financial services.”
McQuade continued: “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.” This emphasis on security and privacy is critical as enterprises navigate the new risks introduced by generative AI.
The trend toward small language models is accelerating. As McQuade told Venture Beat, “You don’t need to go that big for business use cases.” The company reports that it has seen success with models as small as 7 billion parameters (for context, GPT-4 reportedly has trillions of parameters). Arcee’s models are not only faster to deploy and more easily customizable but also run on smaller hardware setups. This efficiency and specialization are vital in diverse applications, from tax assistance to medical question-answering.
Arcee’s technological advancements, such as Model Merging and Spectrum, are also noteworthy. Model Merging combines multiple AI models into a single, more capable model without increasing its size, enhancing efficiency and performance. Spectrum, on the other hand, optimizes training time and reduces the computational resources needed, making it possible for organizations to develop custom AI models more cost-effectively.
“We’re enabling organizations to expand beyond one high ROI use case,” McQuade emphasized. “With our efficiency, they can tackle 10 or 20 use cases.” This ability to rapidly iterate and deploy models at a lower cost could democratize AI access across industries, enabling companies to experiment and optimize AI continuously.
“We’re committed to building value for our investors, partners, and customers,” asserted McQuade. With its recent successes and the rapidly-ballooning VC interest in AI startups, Arcee is set to be not just another player in the GenAI space but a leader guiding the way forward.
Original Source: https://refreshmiami.com/news/arcee-ai-secures-24m-series-a-to-transform-the-landscape-of-small-language-models/
