NeoCognition, a San Francisco startup, has come out of stealth mode after raising $40 million in seed funding. The round was co-led by Cambium Capital and Walden Catalyst Ventures, with Vista Equity Partners also joining.
The company’s angel investors and advisors include Intel CEO Lip-Bu Tan, Databricks co-founder Ion Stoica, and well-known AI researchers Dawn Song, Ruslan Salakhutdinov, and Luke Zettlemoyer.
NeoCognition was started by Yu Su, Xiang Deng, and Yu Gu, who all worked together in Su’s AI agent lab at Ohio State University. Su, a Sloan Research Fellow, and his team created LLM-based agents before ChatGPT, and their research, including Mind2Web and MMMU, is now used by OpenAI, Anthropic, and Google.
NeoCognition’s main idea is inspired by how people learn. “The true power of human intelligence is the ability to continuously learn and specialise. Our approach mirrors how humans gain expertise on the job through building a structured model of their micro-world, and would eliminate the extensive manual customisation required by current models,” says Su.
Rather than building agents for specific industries, NeoCognition is creating general-purpose agents that can specialise on their own. This approach makes agents faster, more affordable, more reliable, and safer in serious situations, without requiring much manual customisation.
Its competitors include Cognition Labs, Adept, and Cohere for specialised agents, and OpenAI, Anthropic, and Google for general-purpose agents. What sets NeoCognition apart is its self-learning design. While most competitors’ agents remain static after launch or require manual updates, NeoCognition’s approach lets agents continue learning and adapting, much like a new employee learning on the job.
“At the core of NeoCognition is a novel learning mechanism that will allow agents to specialise very quickly. We have strong conviction in the team’s expertise and believe their research is charting a new path toward specialised intelligence,” says Landon Downs, Managing Partner, Cambium Capital.
The company will use the seed funding to expand its research, hire more people, and transition from academic work to real-world business applications. They will focus on enterprise tasks that are currently too risky or complex for today’s general AI agents.