Trajectory

Trajectory: Continual Learning for AI Products

Trajectory is a continual-learning platform that helps companies keep improving their agentic AI from real usage instead of shipping a static model. A lightweight SDK captures the signals that product use naturally generates — corrections, edits, re-prompts, and traces — and Trajectory turns those into training that steers model behavior, with a human-approval gate before improvements reach production.

It targets the gap teams hit after launch: the model is good enough to ship, but there is no clean loop to make it better as users interact with it.

Key Features

• Lightweight SDK to capture user signals and traces
• Real-time visibility into AI performance and failure modes
• Behavior steering without manual labeling interfaces
• Continuous training and automatic improvement deployment
• Human-approval gate before production
• SOC 2 with data-governance controls

Ideal Use Case

Trajectory fits teams running agentic or LLM-powered products in production that want a structured way to learn from real interactions and improve quality over time. It suits AI product and ML teams that lack an internal continual-learning pipeline.

How Trajectory differentiates

Trajectory builds the continual-improvement loop — capture, train, approve, deploy — as infrastructure, rather than leaving teams to assemble it from evaluation and labeling tools. It raised a $15M seed with Conviction, Bessemer, and Radical, plus AI leaders Jeff Dean and Fei-Fei Li, and lists users including Clay, Harvey, and Decagon.

FAQ

What is Trajectory? A continual-learning platform that improves AI agents using signals from real production usage.

How does it capture signals? A lightweight SDK records corrections, edits, re-prompts, and traces from real use.

Does it deploy changes automatically? Improvements deploy after a human-approval gate.

Who backs Trajectory? A $15M seed with Conviction, Bessemer, and Radical; users include Clay and Harvey.

tl;dr

Trajectory is a continual-learning platform that improves agentic AI from real production signals via a lightweight SDK, backed by a $15M seed (Conviction, Bessemer, Radical) and used by Clay, Harvey, and Decagon.