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 variables (neurons or directions in the feature space) that drive behaviors like censorship or hallucinations, then dynamically modifying these variables at inference time without altering the model’s weights. This approach allows companies to customize model behavior on the fly without taking systems offline for retraining.

Real-world applications

During his Transform presentation, Gorlla demonstrated two enterprise applications already deployed at a Fortune 20 financial institution:

An email compliance workflow that trains models to understand company-specific acceptable content, allowing analysts to check their emails against compliance standards in real-time. The system highlights potentially problematic content and provides specific explanations.

A brand alignment tool that helps marketers develop copy consistent with brand values. The system can suggest personalized advice on why certain phrases work well for a specific brand and how to improve content that doesn’t align.

“If a company has 900 use cases, they no longer have to fine-tune 900 models,” Gorlla explained. “We’re model-agnostic, so they can just plug us in.”

A real-world example of CTGT’s technology in action was its work with DeepSeek models, where it successfully identified and modified the features responsible for censorship behaviors. By isolating and adjusting these specific activation patterns, CTGT was able to achieve a 100% response rate on sensitive queries without degrading the model’s performance on neutral tasks like reasoning, mathematics and coding.

Images: CTGT presentation at VB Transform 2025

Demonstrated ROI

CTGT’s technology appears to be delivering measurable results. During the Q&A session, Gorlla noted that in the first week of deployment with “one of the leading AI-powered insurers, we saved $5 million of liability from them.”

Another early customer, Ebrada Financial, has used CTGT to improve the factual accuracy of 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. “CTGT has helped improve chatbot accuracy tremendously, eliminating most of those agent requests.”

In another case study, CTGT worked with an unnamed Fortune 10 company to enhance on-device AI capabilities in computationally constrained environments. The company also helped a leading computer vision firm achieve 10x faster model performance while maintaining comparable accuracy.

The company claims its technology can reduce hallucinations by 80-90% and enable AI deployments with 99.9% reliability, a critical factor for enterprises in regulated industries like healthcare and finance.

From Hyderabad to Silicon Valley

Gorlla’s journey is itself remarkable. Born in Hyderabad, India, he mastered coding at age 11 and was disassembling laptops in high school to squeeze out more performance for training AI models. He came to the United States to study at the University of California, San Diego, where he received the Endowed Chair’s Fellowship.

His research there focused on understanding the fundamental mechanisms of how neural networks learn, which led to his ICLR paper and eventually CTGT. In late 2024, Gorlla and co-founder Trevor Tuttle, an expert in hyperscalable ML systems, were selected for Y Combinator’s Fall 2024 batch.

The startup has attracted notable investors beyond its institutional backers, including Mark Cuban and other prominent technology leaders drawn to its vision of making AI more efficient and trustworthy.

Funding and future

Founded in mid-2024 by Gorlla and Tuttle, CTGT raised $7.2 million in February 2025 in an oversubscribed seed round led by Gradient, Google’s early-stage AI fund. Other investors include General Catalyst, Y Combinator, Liquid 2, Deepwater, and notable angels such as François Chollet (creator of Keras), Michael Seibel (Y Combinator, co-founder of Twitch), and Paul Graham (Y Combinator).

“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.”

With AI model size outpacing Moore’s Law and advances in AI training chips, CTGT aims to focus on a more foundational understanding of AI that can cope with both inefficiency and increasingly complex model decisions. The company plans to use its seed funding to expand its engineering team and refine its platform.

Each finalist presented to an audience of 600 industry decision-makers and received feedback from a panel of venture capital judges from Salesforce Ventures, Menlo Ventures, and Amex Ventures.

Read about the other winners Catio and Solo.io. The other finalists were Kumo, Superduper.io, Sutro and Qdrant.

Editor’s note: As a thank-you to our readers, we’ve opened up early bird registration for VB Transform 2026 — just $200. This is where AI ambition meets operational reality, and you’re going to want to be in the room. Reserve your spot now.