Continuously optimizes your website, onboarding, paywalls, and prompts—no dedicated team or massive traffic required.
How it works
The metric to optimize and the guardrails to hold.
What to change, based on your product and your data.
As pull requests you merge, or through a tag on your website.
Learns what works from every user interaction.
A/B → max f(x)
| Manual testing | Levered | |
|---|---|---|
| Setup time | Days to weeks | Minutes |
| Team required | PM + devs + design + analyst | You + Levered |
| Variants tested | 2–4 per optimization | Dozens, in parallel |
| Statistical method | Fixed-horizon tests | Reinforcement learning |
| Optimization | Manual analysis | Autonomous, 24/7 |
| Learning | Starts from scratch | Compounds across optimizations |
How it helps
An agent creates, runs and monitors optimizations on your product. You approve each change from a weekly report.
A factor model explores more variants per unit of traffic than A/B tests. What it learns builds a growing memory the agent draws on.
Every action and learned posterior is available via CLI and agent skills, no UI-only paths. Any coding agent can work with Levered.
Create a free account. Install it with one prompt.
FAQ
Levered is an AI growth engineer that optimizes your product while you build it. It runs variants on your website, paywalls, pricing and prompts, shifts traffic to the best-performing variants, and reports weekly. Underneath is a reinforcement learning loop.
You define a goal metric. Your coding agent connects Levered to your product via CLI and agent skills, and to your data warehouse. Levered creates variants, serves each user the one most likely to perform best, measures lift against a holdout, and carries learnings into the next optimization.
Teams that want to use AI to its full extent in how they grow: AI-native startups after product-market fit, scale-ups that want more and faster optimization without more headcount, and B2B teams with limited traffic, where A/B tests take too long.
With an A/B tool, your team forms the hypotheses and reads the results. Levered makes the decisions at runtime, learns and improves on its own. Multifactor reinforcement learning explores far more variants per unit of traffic.
Levered's algorithms are more data-efficient than fixed-horizon A/B tests. A few thousand visitors per month is enough to test a wide range of variants.
Yes. Each user gets the variant most likely to perform best for their context (device type, user state, traffic source). Without context, Levered finds the globally best variant.
Levered is agent-first: CLI, REST API and agent skills. Any coding agent can install it and connect it to your product.