Key facts
- Trajectory said the round was led by Sequoia, with Nvidia, Bessemer Venture Partners and Bain Capital Ventures participating.
- Trajectory said it has 18 employees and was founded about a year ago by three Stanford classmates.
- Trajectory said Harvey and Rogo use its platform to post-train open-source models for legal and financial work.
- Trajectory named Decagon, Mercor and Nvidia among its other customers.
- Co-founder Michael Elabd said keeping humans in the training process makes Trajectory's approach safer than autonomous self-improvement.
- Trajectory named Thinking Machines Lab, Applied Compute and Prime Intellect as competitors in automated post-training.
Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, announced a $45 million Series A round at a $300 million valuation. Sequoia led the round, with Nvidia, Bessemer Venture Partners and Bain Capital Ventures participating, the company said.
The 18-person company sells an approach it calls continual learning. A business starts with an open-source AI model, connects Trajectory's platform to its applications, and the platform collects feedback on how the model performs. Trajectory then uses that information to retrain the model so it improves at specialized tasks over time, according to the company.
Trajectory said law firm Harvey and financial research company Rogo already use the platform to post-train open-source models, learning from prior tasks such as reviewing a particular type of legal contract or valuing a company with a specific methodology. Decagon, Mercor and Nvidia are also customers, the company said. Trajectory is now making the post-training platform publicly available.
Co-founder Ronak Malde compared foundation models to a very smart PhD student who is always starting a new job. "But what the economy values is someone who worked on the job for 30 years," he told TechCrunch. Malde said lawyers, doctors and other specialists can improve the models near automatically themselves, which could make deploying specialized AI faster and cheaper.
Co-founder Michael Elabd said keeping humans involved in the training process makes Trajectory's approach safer than recursive self-improvement, the idea that AI systems improve their own capabilities, which he said could lead to unintended or even dangerous outcomes. Co-founder Arjun Karanam said the startup built an interface that makes post-training more accessible to non-technical customers.
Trajectory considers Thinking Machines Lab, founded by former OpenAI CTO Mira Murati and behind the fine-tuning platform Tinker, a direct competitor, along with Applied Compute, valued at $3.25 billion in the prior month, and Prime Intellect, which helps companies build and train their own AI agents. Malde cited the team as the startup's biggest advantage; he was among researchers Google DeepMind acqui-hired from Windsurf, Elabd also worked at DeepMind, and Karanam previously worked on generative foundation models at Apple. The startup has also recruited researchers and product designers from OpenAI, Stripe and Netflix, it said.