SAN FRANCISCO: Deep Cogito, a post-training research lab focused on reinforcement learning and self-improvement, on Wednesday announced a $43 million Series A funding round led by TQ Ventures.
The round included participation from Benchmark, Nexus Venture Partners, Atreides Management, South Park Commons and Zscaler, a cloud security company that joined as both a customer and strategic investor. The investment brings Deep Cogito’s total funding to more than $56 million.
The San Francisco-based startup was founded in 2024 by Drishan Arora, who serves as chief executive, and Dhruv Malrana, chief product officer. The co-founders previously worked together at Google on AI Search products, including AI Mode and AI Overviews. Arora led Gemini post-training for AI Search, while Malrana led its product from inception.
Deep Cogito’s research centers on the post-training phase of AI development — the process that turns a pre-trained model into a capable reasoner and teaches it to improve on increasingly difficult tasks. The company’s work includes large-scale reinforcement learning and a technique called Iterated Distillation and Amplification, which allows a model to use additional computation to produce answers beyond what it could generate directly, then distills those improvements back into the model’s weights.
The long-term goal is to build models that progressively improve their own capabilities and ultimately move beyond the limits of human-generated training data.
“Pre-training gives a model an enormous amount of knowledge and capability. Post-training determines what that model can actually become,” Arora said in a statement. “We believe the next frontier is in finding ways for models to improve their own intelligence, internalize those improvements, and become increasingly capable over time.”
Deep Cogito first developed its post-training methods through its Cogito family of open-weight models, which range from 3 billion to more than 600 billion parameters. The same system now powers a platform the company is making available to enterprises that want to build specialized intelligence for their own products using proprietary data.
“Very few teams outside the largest AI labs have demonstrated the ability to post-train models at this scale,” said Schuster Tanger, co-founding partner at TQ Ventures. “Deep Cogito has done that in public through its model releases, and is now bringing the same capability to companies that want intelligence built around their own products.”
Zscaler began working with Deep Cogito as a customer before joining the funding round.
“Frontier models were useful, but they were not enough for the level of specialization we needed,” said Dhawal Sharma, executive vice president of AI security and strategic initiatives at Zscaler. “Deep Cogito stood out because they went deeper than lightweight customization. They worked closely with us to understand our products and the metrics we care about and helped train that intelligence into the model itself.”
Deep Cogito said it will use the new capital to expand its research and engineering team, scale training infrastructure, advance future Cogito model releases and grow its enterprise business.
