The Intent Graph for
the Agentic Internet.
Levered helps businesses understand, predict, and act on commercial intent from humans and AI agents. It forecasts probability and time-to-outcome, recommends the next constrained action, and later checks whether the decision created value.
SCALE London prospecting +£800/day
horizon=14d · fresh=2h
SUCCESS after action
observed=3.9x · lift=+18.2% · supported
Levered helps businesses know who wants what, what happens next, and what to do now.
The old web tracked visitors. The next web must understand intent. Levered fuses human behaviour with agent-declared purpose, forecasts outcomes forward, recommends the next best action, and audits the decision against reality.
First-party signals and declared purpose
Human behaviour, campaign response, CRM movement, orders, outcomes, and AI agent requests enter with consent, provenance, and data rights.
One closed loop from intent to audit
Levered resolves the actor, interprets intent, forecasts the outcome, recommends a constrained action, records the response, and audits what happened.
An honest decision record
Each recommendation carries probability, horizon, interval, segment n, freshness, rationale, expected value, expiry, response, outcome, and audit linkage.
From scattered signals to one closed feedback loop.
The Intent Graph is the platform. The Forecaster is the core intelligence. The Decision Engine converts probability into action. OpenLift is the causal auditor. The Agent Trust Gateway is the frontier.
Resolve who or what is acting
People, anonymous visitors, accounts, campaigns, products, agents, principals, mandates, and workspaces get stable workspace-scoped identity.
Predict what happens next
The Forecaster estimates probability, horizon, time-to-outcome, uncertainty, data freshness, and effective segment support.
Convert probability into action
The Decision Engine recommends scale, cut, hold, wait, retest, route, approve, challenge, reject, or insufficient evidence.
Check whether it created value
OpenLift audits acted-on recommendations against observed outcomes so the feedback loop becomes the moat.
For operators where outcomes are too expensive to wait for before deciding.
Levered is for investors, founders, RevOps, GTM, product, marketing, sales, agency, ecommerce, and agent-facing teams with enough historical outcome data to support honest forecasting.
Teams with live commercial decisions, historical outcomes, and a clear metric they want to improve without waiting weeks to learn what worked.
Teams explaining what will move enterprise value
For investors, founders, and operators who need to connect commercial signals to retention, revenue quality, pipeline risk, product adoption, and board-level operating decisions.
Best fit when the decision is not just what happened, but which driver is likely to matter next.
Teams coordinating pipeline, routing, and market motion
For RevOps, sales, partnerships, and GTM experts who need account progression forecasts, segment readiness, territory pressure, and action ownership in one decision record.
Best fit when pipeline and market decisions need timing, confidence, support, and an audit trail.
Teams deciding what product change will affect outcomes
For product managers connecting activation, adoption, churn risk, feature usage, support signals, and commercial outcomes into forecastable decisions.
Best fit when the product roadmap needs evidence of expected commercial impact, not just usage charts.
Teams spending across paid channels
For teams running Meta, Google, TikTok, affiliates, and lifecycle campaigns who need forecasted campaign and segment outcomes before wasting budget.
Best fit when the team needs to know what to scale, cut, hold, wait on, and later audit.
Multi-client operators selling forward-looking advice
For agency teams moving beyond retrospective reporting into calibrated forecasts, decision audits, and client-facing proof of what changed.
Best fit when retention and strategic value depend on recommendations that can be defended.
Teams with account intent spread across systems
For revenue teams combining website visits, product usage, CRM movement, email engagement, and sales activity to forecast account progression.
Best fit when static lead scoring is too shallow and routing needs time-aware buying intent.
Brands with margin, stock, and demand signals
For brands connecting Shopify or commerce data, stock levels, product margin, regional demand, and campaign activity to forecast conversion and time-to-convert.
Best fit when CAC, ROAS, speed of action, and margin all matter at once.
Merchants receiving machine-initiated requests
For businesses that need to verify who an agent represents, what it wants, what scope it has, and whether to trust the request.
Best fit when agent identity, mandate, purpose, policy scope, and response need an audit trail.
One loop from entity to causal audit.
Every feature exists to move data through the canonical loop: entity, signal, intent, forecast, decision, action, outcome, and causal audit.
Person, account, campaign, product, agent, principal.
First-party event, declared purpose, outcome, or request.
Inferred or declared commercial purpose with context.
Probability, horizon, interval, support, freshness.
Scale, cut, hold, wait, retest, route, approve.
Recorded business move or scoped agent response.
Observed conversion, revenue, margin, pipeline, risk.
OpenLift grades whether the action created value.
The platform for human and agent commercial intent.
People, anonymous visitors, accounts, audiences, campaigns, ads, creatives, products, transactions, experiments, agents, principals, mandates, policy scopes, and workspaces become one graph.
Forecast what happens next. Decide what to do now.
The Forecaster predicts probability and time-to-outcome with calibrated uncertainty. The Decision Engine maps that forecast into scale, cut, hold, wait, retest, route, approve, challenge, reject, or insufficient evidence.
One table can hold investor, RevOps, product, GTM and agent-trust decisions because each row uses the same contract: forecast, support, action, outcome, audit.
| ID | Owner | Prediction | Horizon | Forecast | Support | Decision | Audit |
|---|---|---|---|---|---|---|---|
| INV-04 | Investor | Net revenue retention risk | 30d | 37% · 34-41% | n=1284 | PRIORITISE | observed |
| REV-12 | RevOps | Account progression | 14d | 51% · 46-56% | n=842 | ROUTE | eligible |
| PM-09 | Product | Activation lift | 21d | 44% · 38-49% | n=617 | HOLD | observed |
| GTM-31 | GTM | Segment conversion | 7d | 39% · 35-43% | n=1296 | SCALE | eligible |
| AGT-18 | Agent trust | Request outcome | session | 62% · rules | n=184 | CHALLENGE | policy |
| MKT-07 | Marketing | Creative fatigue | 10d | 28% · 20-36% | n=311 | HOLD | not yet |
▲ DECISION · route REV-12 because 14-day progression forecast is 46-56%, support is n=842, and outcome logging is audit-eligible.
Scale this segment by £800 per day because the 7-day conversion forecast is 34-41%, effective segment support is n=1,284, and OpenLift can later audit the action.
— THAT SENTENCE IS THE PRODUCT.
A forecast is useful only if the loop learns whether it was right.
OpenLift does not create the forward-looking probability. It grades acted-on recommendations after the fact by estimating incremental impact against an appropriate counterfactual.
| Audit ID | Decision | Design | Lift | p-value | Grade | TS |
|---|---|---|---|---|---|---|
| LX-8821 | GTM-31 · scale segment | Region holdout · 14d | +18.2% | p=0.003 | SUPPORTED | 12:04:11 |
| LX-8820 | PM-09 · prioritise fix | Switchback · 10d | +11.9% | p=0.012 | SUPPORTED | 12:03:57 |
| LX-8819 | REV-12 · route account | Observed outcome | +6.4% | p=0.041 | INCONCLUSIVE | 12:02:44 |
| LX-8818 | INV-04 · update plan | Cohort holdout | +4.3% | p=0.110 | UNSUPPORTED | 12:01:32 |
| LX-8817 | MKT-07 · retest creative | Power check | +2.1% | p=0.280 | INVALID | 12:00:08 |
Agent requests need identity, mandate, purpose, and policy.
The first agent product is an inbound trust and policy layer for businesses receiving machine-initiated requests. Learned agent outcome models wait until labelled request-to-outcome data exists.
| REQ ID | Agent | Principal | Action | Scope | Decision | TS |
|---|---|---|---|---|---|---|
| AR-48221 | shop.gpt | acme.uk | price.compare(sku=SK-412) | read+action | VERIFIED | 12:04:11 |
| AR-48220 | atlas.claude | northstar-buyer | checkout.initiate(sku=SK-190) | read | LIMITED | 12:03:58 |
| AR-48219 | hermes.v2 | retail-uk | stock.check(sku=SK-701) | read+action | VERIFIED | 12:03:44 |
| AR-48218 | buyer.aria | london-labs | demo.book(sku=SK-233) | read | CHALLENGE | 12:03:22 |
| AR-48217 | pilot.orion | acme.uk | report.generate(entity=A-442) | read | REJECTED | 12:02:55 |
| AR-48216 | shop.gpt | retail-uk | price.compare(sku=SK-101) | read+action | VERIFIED | 12:02:31 |
Discipline is the product.
The product is the loop: entities emit signals, signals become intent, intent becomes forecasts, forecasts become decisions, actions create outcomes, and outcomes are causally audited.
Levered is not a full customer data platform replacement.
Levered is not another passive analytics or reporting surface.
Levered does not promise perfect attribution or guaranteed predictions.
Alpha decisions are transparent, logged, and reviewed before high-impact actions move.
Forecasts must show support, freshness, model version, uncertainty, and limitations.
Four operators building the system for commercial decision teams.
Levered is not an AI wrapper. It is being built by operators across product, economics, evidence, and engineering, shaped around the work real teams do: reading demand, proving intent, choosing the next action, and giving agents policy-scoped trust with evidence.
Daramola Ben
Founder and product lead
Sets the product thesis: turn human behaviour and agent-declared purpose into calibrated forecasts, constrained decisions, and causal audits.
Maya Ellison
Commercial economics lead
Keeps recommendations tied to demand, margin, willingness to pay, conversion friction, and operational cost.
Theo Adebayo
Evidence and experimentation lead
Turns raw signals into scored evidence, comparisons, and explanations before they become actions.
Nina Patel
Systems engineering lead
Builds the graph, trust policy layer, audit trail, and handoffs so automation stays controlled.
Teams see why an action matters before an agent or workflow runs it.
Budgets, credentials, customer data, and approvals remain explicit.
Every decision adds signal back into the graph for the next one.
Bring one prediction task.
We will prove the loop.
Early access is hands-on. We start with one viable prediction unit, inspect the historical dataset, confirm data rights, generate an honest forecast, record the action, and audit the result.
- Intent Graphalpha
- Forecastercalibration gate
- Decision Enginerules first
- OpenLift Auditoraudit linkage
- Agent Trust Gatewaythin alpha
- Data Rightsrequired