Trajectory
Trajectory provides a self-serve platform for post-training AI models on production data, giving companies the power of a full reinforcement learning research team without the infrastructure burden. Users bring their domain expertise, and Trajectory handles training, evaluation, and deployment in one unified platform that integrates with any agent harness in about five lines of code. The platform includes research previews like Continual Grading, which learns an expert's grading standards from a few examples and applies them across production data.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$100MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 3 rounds
+5$45M · Series A Follow-On
Announced Nov 2026 · from article date
Disclosed cumulative: $100M
Founders
Founder details are not available yet.
Product
Problem
Training and shaping AI models has traditionally required a frontier research lab, a GPU cluster, and years of accumulated expertise, leaving subject matter experts—lawyers, bankers, engineers—without the ability to encode their judgment into the models they use. Companies face climbing token bills and models that miss critical context, while post-training initiatives often take months of R&D and complex infrastructure to get started.
Solution
Trajectory provides a self-serve platform for post-training AI models on production data, giving companies the power of a full reinforcement learning research team without the infrastructure burden. Users bring their value function—their expertise—and Trajectory handles training, evaluation, and deployment in one unified platform. The platform works with any agent harness and requires only about five lines of code to integrate. It includes Continual Grading, a research preview that learns an expert's grading standards from a few examples and applies them across production data, and Group Size 1, which enables training on real production interactions so models keep improving after deployment.
Target Audience
Primary customers are AI product teams and enterprises building on large language models—including legal, financial, and coding tool providers—who need to post-train models on their own production data and domain expertise.
Features
- One-click training and deployment of custom models without managing sandboxes, orchestration, or infrastructure
- Continual Grading (research preview) that learns expert judgment from a subset of traces and scales it across all production data
- Group Size 1 (research preview) enabling training on live production interactions rather than freezing models at launch
- Works with any agent harness with just two code changes to get started
- Built-in evaluation against leading models to identify performance gaps and guide improvements
- SOC 2 certified for enterprise security and compliance