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San Francisco based CtgtA startup that focuses on making AI more trustworthy by model adaptation to the level level level at the level of feature levels. VB transformation 2025 in San Francisco. The company founded by 23-year-old Cyril Gorlla showed how its technology helps companies to overcome AI confidence barriers by changing the model functions instead of using conventional fine votes or quick technical methods.
During his presentation, Gorlla emphasized the “Ai -Doom -Loop”, with which many companies are faced: 54% of the companies, according to Deloitte, state the highest technical risk, while McKinsey reports that 44% of the organizations had negative consequences from the AI implementation.
“A large part of this conference went through the AI doma loop,” said Gorlla during his presentation. “Unfortunately, many of these (AI investments) are not equipped. J & J only canceled Hundreds of AI pilots because they do not really deliver ROI due to a fundamental trust in these systems. “
Break the AI calculation wall
CTGT’s approach represents a significant deviation from conventional AI adaptation techniques. The company was founded on research that Gorlla carried out while he held a foundation chair at the University of California San Diego.
In 2023, Gorlla Published a paper At the international conference on learning representations (ICLR), which describes a method for evaluating and training AI models that were up to 500 times faster than existing approaches and at the same time “three nine” (99.9%) of accuracy.
Instead of relying on brutal scaling or traditional deep learning methods, CTGT has developed what it describes as a “completely new AI stack”, which is fundamentally reinterpreted how neural networks learn. The company’s innovation focuses on understanding and interventions on the characteristic level of AI models.
The company’s approach differs fundamentally from standard interpretability solutions based on secondary AI systems for surveillance. Instead, CTGT offers mathematically verifiable interpretability functions that eliminate the need for additional models and significantly reduce the arithmetic requirements.
The technology identifies specific latent variables (neurons or directions in the characteristic room) that drive behaviors such as censorship or hallucinations and then change these variables dynamically at inference time without changing the weight of the model. With this approach, companies can adapt model behavior in the current flies without making systems offline for retraining.
Applications in practice
During his transformation presentation, Gorlla demonstrated two corporate applications that were already used in a Fortune 20 financial institution:
An email compliance workflow that trains models for understanding company-specific acceptable content and enables analysts to check their emails in real time against compliance standards. The system emphasizes potentially problematic content and provides specific explanations.
A tool made of brand orientations that develop copies that match brand values. The system can suggest personalized advice on why certain phrases work well for a certain brand and how to improve content that is not aligned.
“If a company has 900 applications, it no longer has to hand in 900 models,” said Gorlla. “We are a modelagnagtical so that you can simply connect us.”
An example in the real world for CTGTS technology in action was his work with Deepseek modelsWhere it successfully identified and modified the characteristics responsible for censorship behavior. By insulation and adapting these specific activation patterns, CTGT was able to achieve a 100% response rate to sensitive queries without affecting the performance of the model in neutral tasks such as reasoning, mathematics and coding.
Images: CTGT presentation at VB Transform 2025
Demonstrated Roi
The CTGT technology seems to deliver measurable results. During the Q&A meeting, Gorlla found that in the first week of operation we saved $ 5 million in liability with one of the leading AI-driven insurers. “
Another former customer, Ebrada Financial, used CTGT to improve the factual accuracy of chatbots for customer service. “Hallucinations and other mistakes in chatbot answers have made a high volume of inquiries for live support agents, while customers wanted to clarify the answers,” said Ley Ebrada, founder and tax strategist. “CTGT has contributed to improving the chat bot accuracy enormously and eliminating most of these agent inquiries.”
In another case study, CTGT worked with an unnamed Fortune 10 company to improve the AI functions in arithmetically limited environments. The company also helped a leading computer vision company to achieve 10 -faster model output and at the same time a comparable accuracy.
The company claims that its technology can reduce the hallucinations by 80-90% and enable AI inserts with a reliability of 99.9%, a decisive factor for companies in regulated industries such as healthcare and finance.
From Hyderabad to Silicon Valley
Gorlla’s journey is remarkable. Born in Hyderabad, India, he, he Masted coding At the age of 11 and disassembled laptops in the high school to express more performance for the training of AI models. He came to the United States to study at the University of California in San Diego, where he received the trainee’s scholarship.
His research there focused on understanding the basic mechanisms of learning neural networks, which led to his ICLR paper and finally CTGT. At the end of 2024, Gorlla and co-founder Trevor Tuttle, an expert for hyper-countable ML systems, were selected for the autumn 2024-Charge of the Y combinator.
The startup has drawn remarkable investors beyond its institutional supporters, including Mark Cuban and other prominent technology leaders who are attracted to his vision to make AI more efficient and trustworthy.
Financing and future
Gorlla and Tuttle, Ctgt collected $ 7.2 million In February 2025 in an oversubscribed seed round under the direction of gradients, Google’s AI fund from Google. Other investors are General Catalyst, y Combinator, liquid 2, deep water and remarkable angels such as François Challet (Creator by Keras), Michael Seibel (Y-combinator, co-founder of Twitch) and Paul Graham (Y combinator).
“The start of CTGT is in good time because the industry is fighting how to scale AI within the current arithmetic boundaries,” said Darian Shirazi, managing partner at Gradient. “CTGT eliminates these limits and enables companies to quickly scale their AI deployments and carry out advanced AI models on devices such as smartphones. This technology is crucial for the success of AI deprivation with high operations in large companies.”
With the AI model size that exceeds the law of bogs and progress in AI training chips, CTGT would like to concentrate on a more fundamental understanding of the AI, which can deal with both inefficiency and more complex models. The company plans to use its seed financing to expand its technical team and refine its platform.
Every finalist presented an audience of 600 decision -makers in the industry and received feedback from a jury of risk capital judges from Salesforce Ventures, Menlo Ventures and Amex Ventures.
Read about the other winners CATIO and solo.io. The other finalists were fistPresent Superduper.ioPresent Sutra And Qdrant.
Note from the publisher: As a thank you to our readers, we opened the early book registration for VB Transform 2026-NUR 200 US dollars. Here the AI ambitions meet operational reality and they will want to be in the room. Reserve your place now.
