# Trajectory raises $45M to automate AI model training

> A Stanford startup backed by Sequoia wants to let non-technical experts improve AI models through continuous learning without hiring expensive engineers.
- Title: Trajectory raises $45M to automate AI model training
- Summary: A Stanford startup backed by Sequoia wants to let non-technical experts improve AI models through continuous learning without hiring expensive engineers…
- Keywords: trajectory, ai model training, sequoia, fine-tuning, continual learning, ronak malde, michael elabd, arjun karanam, technology, startups, raises, 45M
- Source: TechCrunch — https://techcrunch.com/2026/11/02/trajectory-raises-45m-to-make-ai-model-training-accessible-to-nontechnical-experts
- Author: Marina Temkin
- Published: 2026-11-02T23:13:53+00:00
- Read time: 1 min
- Topics: trajectory, ai model training, sequoia, fine-tuning, continual learning, ronak malde, michael elabd, arjun karanam, technology, startups
## The problem with AI models today
Current AI models from OpenAI, Anthropic and open source alternatives cannot learn from their mistakes without further training, limiting their usefulness in specialized fields.
## How businesses adapt models now
Companies fine-tune general models for specialized work in law and finance by hiring internal research teams or paying expensive consultants called forward-deployed engineers.
## Trajectory's solution
The Stanford startup founded about a year ago by three classmates with Google DeepMind and Apple experience wants to automate that process using continual learning.
## The funding round
Trajectory's 18-person team raised forty five million dollars in Series A at a three hundred million dollar valuation, led by Sequoia with Nvidia, Bessemer Venture Partners and Bain Capital Ventures.
## The PhD student analogy
Co-founder Ronak Malde compared foundational models to a very smart PhD student always starting a new job, but the economy values someone who worked on the job for thirty years.

> But what the economy values is someone who worked on the job for 30 years.
## How the platform works
Businesses connect Trajectory to their applications, allowing it to collect feedback on model performance. Trajectory then retrains the model to improve at specialized tasks over time.
## Early customers and use cases
Harvey and Rogo use Trajectory to post-train open source models to perform like experienced lawyers or financial analysts. Other customers include Decagon, Mercor and Nvidia.
## Safety through human involvement
Co-founder Michael Elabd argues that keeping humans involved in training makes Trajectory safer than recursive self-improvement, which could lead to unintended or dangerous outcomes.
## Key takeaway

Trajectory raised forty five million dollars to automate AI model training, letting businesses improve specialized models without hiring research teams or consultants.