Trajectory raises $45M to make AI model training accessible to nontechnical experts
The startup's continual learning platform lets businesses retrain open-source AI models using user feedback for specialized tasks.
Trajectory, founded by Stanford graduates with DeepMind and Apple experience, raised $45 million in a Series A at a $300 million valuation led by Sequoia. The 18-person company aims to automate post-training of AI models so non-technical users can improve performance on tasks like legal contracts or financial valuation. Its platform connects to applications to collect feedback and retrain models, with customers including Harvey, Rogo, Decagon, Mercor and Nvidia.
- Raised $45M Series A at $300M valuation
- Uses continual learning from application feedback
- Customers include Harvey, Rogo, Decagon, Mercor, Nvidia
- Interface designed for non-technical users
- Shares elements with recursive self-improvement but keeps humans involved
- Products
- Trajectory
From the original article
3 min read at techcrunch.com
Whether developed by OpenAI, 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.
By Marina Temkin · techcrunch.com
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