Levered gives your agent the infrastructure to optimize and personalize your product in real time.
Drop in the SDK, connect your warehouse. Your agent handles the rest.
| Optimization | Status | Variants | Traffic | Conversion | Lift | Confidence | Users | Segment |
|---|---|---|---|---|---|---|---|---|
| Onboarding FlowOptimizing signup-to-activation path | Active | 23 | 25% | 3.2% | ↑ 12.5% | 95% | 12.3K | New Users |
| Payment Page RedesignTesting checkout layouts and copy | Active | 45 | 50% | 4.8% | ↑ 8.3% | 89% | 8.7K | Premium |
| Pricing Calculator WidgetEnterprise pricing page conversion | Completed | 46 | 100% | 5.1% | ↑ 24.3% | 99% | 34.2K | Enterprise |
| Checkout FlowNew checkout variant exploration | Draft | 29 | 0% | — | — 0% | 0% | 0 | All Users |
| Hero CTA VariantsTesting headline and button copy | Paused | 18 | 0% | 2.1% | ↓ 1.2% | 42% | 3.1K | Returning |
How it works
Set up an optimization once. Then let it explore faster and farther than manual testing ever could.
One command. Connects Levered to your product via MCP.
Describe what you want to optimize. Levered handles variants, allocation, and tracking.
Optimizations run continuously. Levered shifts traffic to winners and explores new variants autonomously.
Capabilities
Advanced models that personalize in real time and remember what works. Agent-managed, warehouse-native.
Works via MCP, CLI, or SDK. Your agent manages the full optimization lifecycle without leaving the terminal.
Multi-armed bandits, contextual bandits, and deep recommenders. All out of the box.
Each user gets the variant most likely to convert, based on their context and behavior.
Every optimization informs the next. Continuously learns what works in which context across all optimizations.
Connects directly to BigQuery and your existing data stack. No data duplication or new pipelines.
Built-in holdout groups let you measure incrementality and prove that optimizations are driving real lift.
Why Levered
Classic platforms are built for manual testing. Levered is built for optimization at agent speed.
| Classic A/B testing | Levered | |
|---|---|---|
| Setup time | Days to weeks | Minutes |
| Team required | PM + Dev + Analyst | Just you + your agent |
| Variants tested | 2–4 per optimization | Dozens, in parallel |
| Statistical method | Fixed-horizon tests | Bandits + deep models |
| Optimization | Manual analysis | Autonomous, 24/7 |
| Learning | Starts from scratch | Compounds across tests |