Building the decision layer for commercial teams.
Levered exists because businesses do not struggle from a lack of data. They struggle to decide what to do with it. We turn first-party commercial signals into evidence-backed recommendations and controlled actions.
The next commercial system is not another reporting layer.
Reporting tells a team what happened. Levered is being built for the harder question: what should change now, what evidence supports it, what impact should the business expect, and who or what is allowed to act?
Commercial teams already have events, dashboards, CRM activity, campaign metrics, commerce records, and reports.
The important work still happens in meetings, spreadsheet reviews, Slack threads, and instinct-led budget changes.
As AI agents request reports, pricing, routing, and commerce actions, businesses need context and permission logic.
A recommendation should explain the signal, the evidence, the expected impact, the risk, and the constraint.
Who we are building with first
The best alpha partners already have commercial signal, a meaningful target, and a team willing to act on evidence. We are starting with teams where the cost of slow or unclear decisions is obvious.
Paid channels are active, spend changes frequently, and incrementality matters.
Account intent lives across web, CRM, product, lifecycle, and sales activity.
Demand, stock, margin, discounting, and campaign pressure need to be read together.
The team wants AI agents to act, but only with clear scope, evidence, and review.
Narrow enough to prove. Real enough to matter.
We do not start by connecting every tool. We start with one commercial goal, the minimum data required to reason about it, and the actions the team is actually prepared to take.
Define the goal
Pick one measurable commercial target and the operating window that matters.
Connect the minimum sources
Start with the few first-party sources required to prove the decision loop.
Map entities and constraints
Resolve campaigns, accounts, products, regions, agents, goals, and business limits.
Run the first recommendations
Review scale, cut, route, hold, retest, and permission decisions with the team.
Log outcomes
Action, dismiss, or revise recommendations so the next cycle becomes sharper.
- 01Build for decisions, not dashboards.
- 02First-party data only. No cross-site tracking. No resale.
- 03Evidence should be visible inside the recommendation, not hidden behind a score.
- 04Humans should understand why a system recommended an action.
- 05AI agents need commercial boundaries before they touch workflow.
- 06Start narrow, prove the loop, then expand the surface area.
First-party data only, scoped to the customer workspace.
Scoped, revocable, and handled as part of onboarding review.
Logged with principal, purpose, evidence, scope, and decision.
Recommendations carry the signals and proof used to produce them.
Alpha partners are onboarded manually while controls mature.
Built for global teams, starting with hands-on design partners.
Request alpha access.
We onboard partners one at a time. The strongest fit is a team with first-party signals, a measurable commercial goal, and a real decision workflow we can improve.