Turn intent into
decisions.
Levered is the decision layer for growth teams. It connects your website, campaigns, CRM, commerce data, and AI agent activity, then recommends the next action with the evidence behind it.
SCALE C-104 by +£800/day
reason=T3 lift · intent↑
SHIFT 12% to London intent
reason=margin ok · lift rising
Levered helps growth teams know what to do next.
Most teams have plenty of data, but the decision still happens in a spreadsheet, Slack thread, or weekly meeting. Levered turns those signals into a recommendation your team can act on, with evidence attached.
Signals from the tools you already use
Web sessions, campaign clicks, CRM changes, orders, margins, stock, support requests, and AI agent activity.
One decision layer between data and action
Levered resolves who the signal belongs to, scores the intent, checks the evidence, and compares it with your commercial goal.
A recommended next move
Your team sees the action, the reason, the expected impact, and the confidence level before changing budget, routing, pricing, or permissions.
From scattered signals to one evidence-backed action.
The product follows the same loop every time: collect the signal, understand who or what it relates to, prove whether it matters, then recommend the next move.
Collect the commercial signals
Website events, ad platforms, CRM activity, Shopify or commerce data, revenue outcomes, and AI agent requests flow into one workspace.
Work out what they mean
Levered connects activity to users, accounts, campaigns, products, agents, and markets so a signal has commercial context.
Check whether it is real
OpenLift tests whether the signal is strong enough to act on using holdouts, baselines, confidence tiers, and causal evidence.
Tell the team what to do
The output is a plain decision: scale, pause, hold, retest, route a lead, generate a report, or approve a scoped agent action.
For teams where commercial data is moving faster than the decision process.
Levered is not for companies looking for another dashboard. It is for teams with live signals, measurable goals, and decisions that need evidence before people or agents act.
A growth, revenue, or commerce team with data in many tools and a clear commercial goal.
Teams spending across paid channels
For teams running Meta, Google, TikTok, affiliates, and lifecycle campaigns who need to know which spend is causing incremental revenue, not just which channel claimed credit.
Best fit when budget changes weekly and the team needs evidence before scaling or pausing.
Teams with account intent spread across systems
For revenue teams combining website visits, product usage, CRM movement, email engagement, and sales activity to understand which accounts are ready for action.
Best fit when lead scoring is too shallow and routing decisions need stronger commercial context.
Brands with margin, stock, and demand signals
For brands connecting Shopify or commerce data, stock levels, product margin, regional demand, and campaign activity to decide what to push, hold, or discount.
Best fit when monthly reporting is too slow for trading, inventory, and growth decisions.
Teams preparing AI agents to act safely
For operators who want AI agents to request reports, pricing, stock checks, lead routing, or checkout actions without giving them open-ended control.
Best fit when every agent action needs purpose, scope, evidence, and approval logic.
One loop from raw signal to permitted action.
Every feature exists to move data through the same path: capture the signal, resolve the entity, score the intent, attach evidence, recommend a decision, and control the action.
Website, CRM, campaign, source, or agent event.
User, account, campaign, product, agent, market.
Buying · research · market · risk · agent request.
OpenLift causal proof. Tiered confidence.
Scale · cut · hold · retest · route · approve.
Budget, routing, report, or permissioned agent action.
A single queryable graph of every commercial actor, signal, and outcome.
Users, accounts, agents, campaigns, creatives, regions, products, revenue — every entity is a node, every relationship a signal.
Set a business goal. Get the next action.
Set a revenue, CAC, ROAS, pipeline, or account-readiness target. The engine reads live intent and evidence, then outputs the specific changes most likely to move the goal.
| ID | Campaign | Channel | Intent | Lift | Δ / day | Action |
|---|---|---|---|---|---|---|
| C-104 | London · Prospecting | Meta | 82 | T3 | +£1,600 | ▲ SCALE |
| C-118 | Brand · US | 74 | T2 | +£700 | ▲ SCALE | |
| C-133 | Retargeting · Cart | Meta | 58 | T2 | +£300 | ▲ SCALE |
| C-141 | New York · Wellness | TikTok | 22 | T1 | £-325 | ▼ CUT |
| C-152 | Search · Non-brand | 68 | T3 | +£1,125 | ▲ SCALE | |
| C-160 | Broad · APAC | Meta | 14 | T1 | £-300 | ▼ CUT |
▲ RECOMMENDATION · scale C-104 by +£1,600/day because London demand intent is rising, causal lift verified at Tier 3, and you are currently 14% behind your revenue target.
Scale this campaign by £800 per day because demand intent in this segment is rising, the lift is causally verified, and you are currently 23% behind your revenue target.
— THAT SENTENCE IS THE PRODUCT.
Causal proof beats correlation. Attribution is a story — lift is the evidence.
OpenLift runs holdouts, PSA baselines, ghost bids, and switchbacks in the background. Every recommendation carries an evidence tier, not just a dashboard number.
| Exp ID | Entity | Method | Lift | p-value | Tier | Status | TS |
|---|---|---|---|---|---|---|---|
| LX-8821 | Meta / London / prospecting | Region holdout · 14d | +18.2% | p=0.003 | T3 | PROVEN | 12:04:11 |
| LX-8820 | TikTok / New York / interest | PSA baseline · 7d | +2.1% | p=0.280 | T1 | REJECTED | 12:03:57 |
| LX-8819 | Google / US brand | Ghost bid · 21d | +6.4% | p=0.041 | T2 | PROVEN | 12:02:44 |
| LX-8818 | Shopify / cart flow | Switchback · 10d | +11.9% | p=0.012 | T3 | PROVEN | 12:01:32 |
| LX-8817 | Meta / retargeting | Holdout · 14d | +4.3% | p=0.110 | T2 | RUNNING | 12:00:08 |
AI agents need permission. Intent supplies the context.
AI agents can request pricing, stock, comparison, report generation, lead routing, or checkout actions. Levered verifies purpose, principal, scope, evidence, and permission before anything moves.
| REQ ID | Agent | Principal | Action | Scope | Decision | TS |
|---|---|---|---|---|---|---|
| AR-48221 | shop.gpt | acme.uk | price.compare(sku=SK-412) | read+action | GRANT | 12:04:11 |
| AR-48220 | atlas.claude | northstar-buyer | checkout.initiate(sku=SK-190) | read | REVIEW | 12:03:58 |
| AR-48219 | hermes.v2 | retail-uk | stock.check(sku=SK-701) | read+action | GRANT | 12:03:44 |
| AR-48218 | buyer.aria | london-labs | demo.book(sku=SK-233) | read+action | GRANT | 12:03:22 |
| AR-48217 | pilot.orion | acme.uk | report.generate(entity=A-442) | read | REJECT | 12:02:55 |
| AR-48216 | shop.gpt | retail-uk | price.compare(sku=SK-101) | read+action | GRANT | 12:02:31 |
Discipline is the product.
The product is the loop: signals resolve into entities, intent becomes evidence, evidence becomes decisions, and humans or agents act with permission.
Dashboards report. Levered decides.
Analytics tells you what happened. Levered tells you what to do next.
Attribution tells you which click got credit. Levered tells you what actually caused revenue.
Levered does not replace Meta, Google, or your CRM. It tells your team what to change, why, and with what confidence.
Reports are generated from evidence, signals, and decisions. They are not a monthly screenshot exercise.
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 permission with evidence.
Daramola Ben
Founder and product lead
Sets the product thesis: turn commercial intent into evidence-backed decisions that teams and agents can act on.
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, permission 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 us a goal.
We will bring the numbers.
Early access is hands-on. We onboard teams one at a time, connect real sources, and tune the signal-to-decision loop against your data.
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- Latency41ms p95