Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, announced Monday that it has raised $45 million in a Series A round at a $300 million valuation. Sequoia led the round, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital Ventures. The 18-person company is making its post-training platform publicly available.
The startup is targeting a limitation shared by AI models from OpenAI, Anthropic, and open source alternatives: they do not learn from their mistakes without further training. Businesses adapting models for specialized fields such as law and finance typically fine-tune general models, which often requires internal research teams or expensive consultants known as forward-deployed engineers. Trajectory aims to automate that process so models can learn from feedback and continuously improve at specialized tasks.
Trajectory's approach is known as continual learning. Customers start with an open source AI model and connect the platform to their applications, which collects feedback on how the model performs. Trajectory then uses that information to retrain the model, helping it improve at specialized tasks over time. Co-founder Ronak Malde said lawyers, doctors, and other specialists can improve the models nearly automatically themselves, a capability that could make deploying specialized AI faster and cheaper. Malde compared foundational models to a very smart PhD student who is always starting a new job, while he said the economy values someone who has worked on the job for 30 years.
Companies including Harvey and Rogo are already using Trajectory's platform to post-train open source models so they perform more like experienced lawyers or financial analysts, learning from prior tasks such as reviewing a particular type of legal contract or valuing a company using a specific methodology. Trajectory's other customers include Decagon, Mercor, and Nvidia. Malde said the people using Trajectory decide what good looks like for their organization and can turn that into improvements for their model.
Trajectory's approach shares some similarities with recursive self-improvement, the idea that AI systems can improve their own capabilities. Co-founder Michael Elabd argues that keeping humans involved in the training process makes Trajectory's approach safer, since autonomous self-improvement could lead to unintended or even dangerous outcomes.
Trajectory is not alone in automating post-training. Competitors include Thinking Machines Lab, founded by former OpenAI CTO Mira Murati; Applied Compute, the company behind the fine-tuning platform Tinker, which was valued at $3.25 billion last month; and Prime Intellect, a platform that helps companies build and train their own AI agents. Malde said the startup's biggest advantage is its team. He was among the researchers Google DeepMind acqui-hired from Windsurf, Elabd also worked at DeepMind, and third co-founder Arjun Karanam previously worked on generative foundation models at Apple. The startup has also recruited researchers and product designers from OpenAI, Stripe, and Netflix. Karanam said the company developed an interface that makes post-training more accessible to its nontechnical customers.
More company and startup news from TechManNews.
