At a glance
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Trajectory.ai captures specific user corrections—like exact edits after an agent's output—to fine-tune models continuously. This turns product usage into a real-time feedback loop that creates faster adaptation and competitive moats.
Continuous learning from user corrections can create product moats and faster adaptation than periodic retraining.
Summary
Most AI models are frozen at deployment, repeating the same mistakes. Trajectory.ai is productizing continual learning for enterprises, taking user corrections and agent interactions to fine-tune models automatically. Their platform currently serves legal and GTM companies, with Harvey using an NVIDIA NeMo model fine-tuned on expert legal tasks.
The approach isn't just about RL or binary feedback—Trajectory captures specific corrections (e.g., 'button should be on the left') and uses self-distillation policy optimization (SDPO) to guide model updates. They've also open-sourced a training stack that enables concurrent, dynamically scheduled LoRA jobs, solving a systems bottleneck.
Early results show faster, cheaper models that improve across key metrics. But the real challenge is making this legible and controllable for product managers, not just researchers. Governance, privacy, and the risk of overfitting to narrow feedback loops remain open questions.
For engineering leaders, the signal is clear: the feedback loop is the new competitive moat. Companies that can operationalize user signal into continuous model improvement will outpace those that still treat models as static artifacts. The infrastructure and talent needed will be a new battleground.
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