CTGT Secures $7.2M to Accelerate AI Model Deployment

Feb 21, 2025
CTGT has raised $7.2 million in a seed round to enhance its AI model deployment platform, which promises speeds up to 500 times faster, according to Global Newswire.

CTGT has raised $7.2 million in an oversubscribed seed round to advance its platform for customizing, training, and deploying AI models, announced in a press release. The platform, based on research from the University of California San Diego, claims to be up to 500 times faster than current methods.

The funding round was led by Gradient, Google's early-stage AI fund, with participation from General Catalyst, Y Combinator, and several notable angel investors. This investment will be used to accelerate product development and expand sales and marketing efforts.

CTGT's technology allows enterprises to scale AI deployments and run advanced models on devices like smartphones. It also offers the ability to automatically refine and retrain models in production environments, addressing issues such as hallucinations and data leakage without taking models offline.

Founded in mid-2024 by Cyril Gorlla and Trevor Tuttle, CTGT is already collaborating with a Fortune 10 company and other enterprise clients to enhance AI safety and deployment. The company's approach aims to overcome the current limits of AI compute, enabling more efficient and accurate AI model performance.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like AI Funding Brief or Daily AI Brief.

Also, consider following us on social media:

AI Funding News AI Brief AI Brief (X)
Free newsletter

AI Programming Weekly

Weekly news about AI tools for software engineers, AI enabled IDE's and much more.

Guide
AI app builders

AI app builders

Tools that turn a description into a working web or mobile app, and the platforms companies use to build internal tools with AI

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.