Whether developed by OpenAI or Anthropic or built on open source alternatives, current AI models share a critical limitation: They don’t learn from their mistakes without further training.

Businesses adapting these models for specialized fields, such as law and finance, typically get around this by fine-tuning general models. However, that often requires hiring internal research teams or relying on expensive consultants, commonly known as forward-deployed engineers (FDEs).

Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, wants to automate that process, allowing AI models to learn from feedback and continuously improve at specialized tasks.

The 18-person company just announced that it raised $45 million in a Series A round at a $300 million valuation, led by Sequoia, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital Ventures.

Trajectory co-founder Ronak Malde compared foundational models to a very smart PhD student who’s always starting a new job. “But what the economy values is someone who worked on the job for 30 years,” he told TechCrunch.

The startup aims to solve this problem through an approach known as continual learning. Businesses using Trajectory can start with an open source AI model and connect the platform to their applications, allowing it to collect feedback on how the model performs. Trajectory then uses that information to retrain the model, helping it improve at specialized tasks over time.

With Trajectory, lawyers, doctors, and other specialists can improve the models near automatically themselves, Malde said.

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