Trajectory raises $45M to make AI model training accessible to nontechnical experts, and more

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0:50So you can focus on other important things, like savoring that last bit of pumpkin pie. From early startups to Fortune 500 companies, thousands are turning to Framer to build a website they'll love. Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com slash tcstartups for 30 % off a Framer Pro annual plan. That's framer.com slash tcstartups for 30 % off. Rules and restrictions may apply. Whether developed by OpenAI or Anthropic, or built on open-source alternatives, current AI models share a critical limitation. They do not learn from their mistakes without further training.

1:36Businesses 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 or 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.

2:27The company's co-founder compared foundational models to a very smart Ph.D. student who's always starting a new job. The startup aims to solve that 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 info 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. That capability could make deploying specialized AI faster and cheaper.

3:10Companies like Harvey and Rogo are already using Trajectory's platform to post-train open-source models, helping them perform more like experienced lawyers or financial analysts by learning from previous tasks, such as reviewing a particular type of legal contract or valuing a company using a specific methodology. Trajectory's other customers include Decagon, Mercore, and NVIDIA. The startup is now making its post-training platform publicly available. The people using Trajectory decide what good looks like for their organization, and can turn that into improvements for their model. Trajectory's approach to continual learning shares some similarities with recursive self-improvement, the idea that AI systems can improve their own capabilities.

4:02but another co-founder argues that keeping humans involved in the training process makes Trajectory's approach safer, since autonomous self-improvement could lead to unintended or even dangerous outcomes. Trajectory is not the only startup trying to automate post-training. Other companies going after this market include Thinking Machines Lab, founded by former OpenAI CTO Miramarati, and the company behind the fine-tuning platform Tinker, Applied Compute, which was valued at$3.25 billion last month, and Prime Intellect, a platform that helps companies build and train their own AI agents.

4:47Yvonne Bajella knew it was time to launch her own fund. She spent more than a decade as a venture capitalist, most recently at LocalGlobe, where she made bets on now-unicorn companies like the fintech Wise, the insurance company Marshmallow, and RoboTaxi Moves. Well, on Tuesday, she announced that her own fund, Evertrue, officially closed an oversubscribed$17 million fund I. She said, After more than a decade backing founders who bet everything on their ideas, it felt like time to make a bet on my own. I wanted the independents to act on those convictions and build something that reflected my own approach to investing.

5:30The firm, which has already backed seven companies, will focus on early-stage investing across the UK and Europe. The average check size will be around£350 ,000, she said, with plans to back at least 20 companies over the next three years. Perhaps unsurprisingly, she said this AI boom helped her realize that this was the right time to launch her own fund. Not only has it changed the economics of launching a company, she said, but it has created new opportunities for early-stage investors like herself. The ability to build has been democratized, she said, but the ability to build something enduring remains rare.

6:11As TechCrunch has previously reported, the UK's startup scene is having a moment. King's Cross, for example, is one of the world's top AI hubs, while companies like Revolut, Synthesia, Eleven Labs, and the Bezos-backed Cusp AI are redefining what it means to scale and attract success in the UK market. For some like Bajella, this moment means there is an opportunity to snap up investments in the next generation of top British talent early. However, this is also a tough market for emerging fund managers. In the U.S., it's been described as a barbell market, where capital is concentrated at the top and among firms led by those who already have solid track records.

6:56Things may be slightly rosier in the UK, as pitchbook data through the end of September shows that emerging fund managers in this region raised$7.4 billion, already surpassing the$6.2 billion they raised all of last year. Still, Magella said fundraising was a grind. The hardest rejections were not necessarily the outright no's, saying they were the ones that came after months of conversations and significant time invested only for the answer to ultimately be no. Time is one of the most valuable resources a founder has, and a prolonged process without clarity has a real cost. Still, she said the fund nabbed five institutional backers, including the British Business Bank and Basecamp, the fund of funds of the investment firm Phoenix Court, which is also home to Local Globe.

7:49Evertrue was part of the£400 million British Business Bank's Investor Pathways Capital Initiative, which saw it pour£90 million to 10 first-time fund managers. Magella said Evertrue wanted to raise around$13 million, but saw a lot of investor interest. She decided to turn down additional investors rather than expand the fund size. She said, Fund size is a strategic decision, not a measure of success in itself. I wanted enough capital to build meaningful positions in my highest conviction investments without creating pressure to deploy capital simply because it was available. Around 80 % of Evertrue's LPs are UK-based, with the rest scattered across Europe, Asia, and the U.S.

8:37She said her international LP base represents the relationships she's built over more than a decade investing across markets. It also reflects the fund's global ambition, she said, especially since so many British founders keep their eyes on a U.S. expansion. More than half her portfolio already operates in the U.S. Evertrue will invest mainly across AI and fintech. And she added that she's got particular interest in people with a unique insight into a problem, an unusual ability to execute, and the ambition to build something much bigger than their initial product. I pay close attention to the distance a founder has traveled, she said, what they've overcome, and how that has shaped their perspective.

9:24That's all for now. For the latest in startup tech news, go to TechCrunch.com.

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