CTGT Raises $7.2M to Revolutionize AI Compute with 500x Faster Model Training

CTGT Raises $7.2M To Help Enterprises Break Through the Limits of AI Compute, with a New Approach to Customizing, Training and Deploying Models That's 500x Faster

Enter your email to get Benzinga's ultimate morning update: The PreMarket Activity Newsletter SAN FRANCISCO, Feb. 20, 2025 (GLOBE NEWSWIRE) -- CTGT, enabling enterprises to finally scale their AI efforts with a new approach to customizing, training and deploying AI models that's up to 500x faster, announced today that it has raised an oversubscribed $7.2M seed round to accelerate product development and sales & marketing. Gradient, Google's early-stage AI fund, led the round with participation from General Catalyst, Y Combinator, Liquid 2, Deepwater and notable angels including François Chollet (Google, creator of Keras), Michael Seibel (Y Combinator, co-founder Twitch), Paul Graham (Y Combinator), Peter Wang (co-founder Anaconda), Wes McKinney (creator of Pandas), Mike Knoop (co-founder Zapier), Kulveer Tagger (Zeus Living), Andrew Miklas (co-founder PagerDuty) and Taner Halicioglu (first full-time Facebook employee). This is the company's first funding round. As enterprises seek to move their AI projects from proof-of-concept to production at scale and move from low-risk use cases like chatbots to high-risk ones like security, the limits of AI compute have become apparent. AI requires enormous (and growing) amounts of compute and energy. Model developers speak of AI hitting the wall on compute, limiting what AI can do. CTGT co-founder & CEO Cyril Gorlla has been pondering this challenge for years and made it the focus of his research for his endowed chair at the University of California at San Diego. In 2023, he published a seminal paper on the topic, presented at ICLR, that described a new way of evaluating and training AI models that was up to 500x faster and resulted in three nines of accuracy - a huge leap over current methods. That methodology became the basis for CTGT. "CTGT's launch is timely as the industry struggles with how to scale AI within the current confines of computing limits," said Darian Shirazi, Managing Partner at Gradient. "CTGT removes those limits, enabling companies to rapidly scale their AI deployments and run advanced AI models on devices like smartphones. This technology is critical to the success of high-stakes AI deployments at large enterprises." While many other vendors can identify model problems, only CTGT can automatically refine and retrain models on the fly in production environments - eliminating the need to take models offline for updates. Enterprises can use CTGT to ensure that AI models perform in line with their policies, including privacy, security and corporate standards guidelines - even as environments change. CTGT can help companies respond to changes in customer demand by giving models more autonomy or being more restrictive to security issues when new threats emerge. For instance, if an enterprise faced an emerging online security threat such as a prompt engineering attack, CTGT could recognize that and adjust a model on the fly to resist the attack. CTGT can also detect and fix hallucinations, inaccuracy and data leakage. CTGT's Gorlla said, "The lack of certainty and trust in models' output is a significant barrier to adoption in high-stakes industries like healthcare and finance, where AI can make the biggest difference. By greatly improving accuracy, CTGT is removing that barrier." Founded in mid-2024 by Gorlla and co-founder Trevor Tuttle, CTGT is already working with a Fortune 10 company to deploy safe, on-device AI, and has landed enterprise customers who are already relying on CTGT software to close the gaps between AI safety and deployment. One of CTGT's first customers is Ebrada Financial, which leveraged CTGT to improve factual accuracy of its frontline customer service chatbots. "Previously, hallucinations and other errors in chatbot responses drove a high volume of requests for live support agents as customers sought to clarify responses," said Ley Ebrada, Founder and Tax Strategist at Ebrada Financial. "CTGT has helped improve chatbot accuracy tremendously, eliminating most of those agent requests. We're very happy with the performance." About CTGT CTGT enables enterprises to finally scale their AI efforts, with a new approach to customizing, training and deploying models that's up to 500x faster with three nines of model accuracy. Based on technology developed at the University of California San Diego, CTGT's enterprise AI risk management and deployment platform removes the current limits on AI compute and is the first to evaluate model quality in live environments and automatically update models for better, more accurate performance. Learn more at CTGT.ai. Media contact: Michelle Faulkner Big Swing 617-510-6998 michelle@big-swing.com A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/5ebf96b3-d0eb-4b34-9392-dc55e915bd48 Market News and Data brought to you by Benzinga APIs

Hyderabad-based Founder's CTGT Raises $7.2M to Help Enterprises Scale AI Beyond Deep Learning

"CTGT's vision is to make AI more transparent and accessible without sacrificing on performance." CTGT, an AI startup backed by Y Combinator (YC F24), has secured $7.2 million in funding to drive its mission of scaling AI beyond traditional deep learning. The round was led by Gradient, Google's early-stage AI fund, with participation from General Catalyst, Liquid 2 Ventures, and Y Combinator. The startup has also garnered support from leading AI figures, including François Chollet (Keras), Paul Graham (Y Combinator), Peter Wang (Anaconda), Michael Seibel (Twitch), Mike Knoop (Zapier), and Wes McKinney (Pandas). Haling from Hyderabad, 23-yr old Cyril Gorlla's startup CTGT has also attracted funds from prominent investor Mark Cuban. "CTGT's vision is to make AI more transparent and accessible without sacrificing on performance," said Gorlla in an earlier exclusive interaction with AIM. CTGT aims to address the growing inefficiencies in deep learning, a challenge that has persisted despite rapid advancements in AI models. Gorlla, who has long studied AI's increasing demand for compute, believes that merely scaling models will not resolve their fundamental limitations. Instead, CTGT has developed a new AI stack that transforms how models learn and train. The company claims its platform can customise, train, and deploy AI models up to 500 times faster than traditional methods, all while maintaining state-of-the-art accuracy. More importantly, this is achieved without requiring massive computational power, a significant departure from conventional deep learning approaches. CTGT's AI deployment and quality platform is already in use by Fortune 10 enterprises, helping them gain more control over AI models in real-world applications. With fresh funding, the company plans to expand access to more enterprises looking to move AI from proof-of-concept to full-scale production. Gorlla had told AIM that many existing AI methods remain computationally inefficient, citing an example where a leading foundation model provider's state-of-the-art LLM interpretability requires more compute than the foundation model itself, making such methods inaccessible to most companies. By focusing on understanding the foundational mechanisms of learning, CTGT is developing AI models that are both efficient and interpretable. The company's approach has been evaluated across multiple benchmarks. In a test involving 121 classification datasets, traditional neural networks required five hours for training, whereas CTGT's method completed the process in just 40 minutes. The company is now working on an upgraded training algorithm, which, according to Gorlla, will be 500 times faster than the current version. "This is just the beginning of our journey in creating the next generation of truly intelligent AI: built from the ground up to be trustworthy and efficient, dynamically adapting to your needs," he said in a Linkedin post.