Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more
San Francisco-based CTGT, a startup focused on making AI more trustworthy through feature-level model customization, won the Best Presentation Style award at VB Transform 2025 in San Francisco. Founded by 23-year-old Cyril Gorlla, the company showcased how its technology helps enterprises overcome AI trust barriers by directly modifying model features instead of using traditional fine-tuning or prompt engineering methods.
During his presentation, Gorlla highlighted the “AI Doom Loop” faced by many enterprises: 54% of businesses cite AI as their highest tech risk according to Deloitte, while McKinsey reports 44% of organizations have experienced negative consequences from AI implementation.
“A large part of this conference has been about the AI doom loop” Gorlla explained during his presentation. “Unfortunately, a lot of these [AI investments] don’t pan out. J&J just canceled hundreds of AI pilots because they didn’t really deliver ROI due to no fundamental trust in these systems.”
Breaking the AI compute wall
CTGT’s approach represents a significant departure from conventional AI customization techniques. The company was founded on research Gorlla conducted while holding an endowed chair at the University of California San Diego.
In 2023, Gorlla published a paper at the International Conference on Learning Representations (ICLR) describing a method for evaluating and training AI models that was up to 500 times faster than existing approaches while achieving “three nines” (99.9%) of accuracy.
Rather than relying on brute-force scaling or traditional deep learning methods, CTGT has developed what it calls an “entirely new AI stack” that fundamentally reimagines how neural networks learn. The company’s innovation focuses on understanding and intervening at the feature level of AI models.
The company’s approach differs fundamentally from standard interpretability solutions that rely on secondary AI systems for monitoring. Instead, CTGT offers mathematically verifiable interpretability capabilities that eliminate the need for supplemental models, significantly lowering computational requirements in the process.
The technology works by identifying specific latent variab …