Trajectory raises $45M to make AI model training accessible to nontechnical experts — SkimNews
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- Trajectory raised a $45 million Series A at a $300 million valuation, led by Sequoia with participation from Nvidia, Bessemer Venture Partners and Bain Capital Ventures.
- Trajectory’s continual-learning platform starts with an open-source model, connects it to a customer’s applications, collects performance feedback and retrains the model to improve specialized tasks over time.
- Harvey and Rogo use Trajectory to post-train open-source models for legal-contract review and company valuations under specific methodologies; Decagon, Mercor and Nvidia are also customers.
- Arjun Karanam says Trajectory’s interface makes post-training accessible to nontechnical customers; the startup has made its platform publicly available, and Ronak Malde says specialists can improve models nearly automatically themselves.
- Michael Elabd says keeping humans involved distinguishes Trajectory’s approach from recursive self-improvement and is safer because autonomous self-improvement can lead to unintended or dangerous outcomes.
- Thinking Machines Lab is a direct competitor through its Tinker fine-tuning platform; Trajectory also names Applied Compute, valued at $3.25 billion last month, and Prime Intellect as rivals.
- Trajectory was founded about a year ago by Stanford classmates Ronak Malde, Michael Elabd and Arjun Karanam, whose backgrounds include Google DeepMind and Apple, and has recruited researchers and product designers from OpenAI, Stripe and Netflix.
Why it matters: For lawyers, doctors and other specialists, Trajectory says its approach targets faster, cheaper model deployment with less reliance on internal research teams or expensive FDE consultants. With a $45 million Series A behind it, Trajectory is now competing publicly through its human-guided workflow against Thinking Machines Lab’s Tinker, Applied Compute and Prime Intellect.
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