Building the operating layer for commercial intent across the agentic internet.
Levered exists because the old web tracked visitors, while the next web must understand intent from humans and agents. We are building commercial intent infrastructure for investors, operators, RevOps, GTM, product, commerce, agencies and agent-facing teams: forecast outcomes, recommend constrained actions and audit whether those actions created value.
The Intent Graph is the platform. The feedback loop is the moat.
Levered is being built to answer five questions better than existing systems: who or what is acting, what they appear to want, what is likely to happen next, what the business should do now, and whether the action created incremental value. That applies to board operating decisions, RevOps routing, GTM timing, product roadmaps, media spend, commerce trade-offs and agent trust.
Investors, founders, RevOps, GTM, product, commerce, agency and agent-facing teams already have events, CRM activity, campaign metrics, product usage, commerce records, agent requests and reports.
The old web tracked identity proxies such as cookies, devices, sessions, logins and form fills. Those do not reliably explain commercial purpose, operating priority or the decision a team should take next.
Agents may arrive with structured purpose, delegated authority and a requested action. They need trust policy, not generic analytics.
Forecast, decision, action, outcome and causal grade history is more defensible than any single model or dashboard.
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 where the cost of slow or unclear decisions is obvious: capital allocation, pipeline progression, product prioritisation, campaign spend, margin decisions, client recommendations and agent-request policy.
Operating signals need to connect to enterprise value, retention risk, revenue quality and resource allocation.
Account intent, territory pressure, pipeline movement and market timing live across too many systems.
Feature usage, onboarding, support signals and churn risk need to connect to commercial impact.
Strategic value depends on forward-looking recommendations, not retrospective reporting.
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.
Machine-initiated requests need identity, mandate, purpose, scope and policy decisions.
One complete loop before breadth.
We do not start by connecting every tool. We start with one prediction and one decision it should improve, then prove whether the forecast can change a real outcome for a real operating team.
Define one prediction unit
Choose the outcome, timestamp, horizon, segment granularity and one decision it should improve.
Inspect historical data
Validate labels, non-converters, censoring, features, data freshness and legal rights.
Assess support and baselines
Separate eligible and ineligible segments before modelling or recommending action.
Generate one honest forecast
Show probability, horizon, interval, segment n, freshness, rationale and model version.
Record action and audit
Log whether the user acted, observe the outcome, and attach OpenLift audit where valid.
- 01Build one complete forecast-decision-action-outcome-audit loop before breadth.
- 02First-party data only. No cross-site tracking. No resale.
- 03Never express more certainty than the data supports.
- 04Every forecast needs support, freshness, model version, interval and rationale.
- 05AI agents need identity, principal, mandate, purpose, scope and policy before workflow.
- 06Data rights are a gate, not a footnote.
First-party data only, scoped to the customer workspace.
Ownership, licence, permitted purpose and retention terms documented before model use.
Inputs, output, support, model version and explanation are retained for reproducibility.
Logged with identity, principal, mandate, purpose, scope, risk and policy response.
Tenant boundaries, service authorisation and audit logs are required controls.
Model cards cover intended use, limitations, data windows and calibration.
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: investor operating cadence, RevOps routing, GTM timing, product prioritisation, commerce trading, agency recommendations or agent trust.