The closed-loop architecture for commercial intent infrastructure.
Levered is one connected system, not a bundle of dashboards. It captures first-party signals, resolves entities, forecasts outcomes, recommends constrained actions, records execution, observes outcomes, runs causal audits and recalibrates.
It proves one honest prediction before expanding the surface.
Levered is not a replacement for your CRM, ad platforms, warehouse, or commerce stack. Alpha starts narrower: one partner dataset, one viable prediction task, one useful forecast, one action, and one audit path that shows whether Levered was right.
Start with one viable prediction unit
Alpha begins by defining the outcome, timestamp, horizon, segment support, baseline, labels, data rights, and one decision the forecast should improve.
Resolve signals into the Intent Graph
The platform attaches activity to people, anonymous visitors, accounts, products, campaigns, regions, transactions, experiments, agents, principals, mandates, policy scopes, forecasts, decisions, and outcomes.
Predict probability and timing honestly
The Forecaster shows probability, horizon, interval, effective segment support, freshness, rationale, model version, and insufficient-evidence states.
Close the loop after action
Decision responses, observed outcomes, OpenLift audit grades, and training eligibility flow back into the evaluation store.
Platform layers
Each layer has a specific job. Together they create the path from first-party signal to calibrated forecast, constrained action, observed outcome, causal grade and model learning.
Authentication, tenant boundary, workspace-isolated data model and members.
Manual/CSV first, server SDK later, browser pixel after consent and debugger support.
Schema mapping, outcome/time-to-outcome labels, rights confirmation and data quality.
Entities, events, relationships, purpose, policy scope, forecasts, decisions and outcomes.
Probability, horizon, interval, effective segment support, calibration and model versioning.
Transparent rules that produce scale, cut, hold, wait, retest, route or trust decisions.
Causal grade, incremental effect, credible interval, validity checks and training eligibility.
Inbound agent verification, principal, purpose, scope, mandate, risk and policy response.
From entity to causal audit, with control at every step.
The platform keeps a clear chain of reasoning so teams can reproduce a historical forecast, inspect its support and model version, and understand why it should or should not be actioned.
Entity
The actor is resolved as a human, account, campaign, product, agent, principal, mandate, policy scope, or workspace object.
Signal
A first-party event, declared purpose, historical outcome, or agent request enters with consent, provenance and rights context.
Intent
The graph records inferred or declared commercial purpose, requested action, signals, policy scope and freshness.
Forecast
The Forecaster returns probability, horizon, expected time-to-outcome, interval, support and model version.
Decision
Transparent rules convert the forecast into scale, cut, hold, wait, retest, route, approve, challenge, reject or insufficient evidence.
Action
The user accepts, modifies, rejects or ignores the recommendation, and the actual business action is recorded.
Outcome
The observed conversion, revenue, margin, pipeline change, agent outcome or non-conversion is linked back.
Causal audit
OpenLift marks the result supported, unsupported, inconclusive or invalid and decides whether it can train future models.
| 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 |
POST /v1/agents POST /v1/agent/request GET /v1/agents/:id/mandate GET /v1/intent/:entity_id GET /v1/forecasts/:entity_id GET /v1/decisions/:entity_id // scopes commerce.read commerce.action intent.query forecast.query decision.query
Agent trust is not a universal identity network. In alpha it is a thin inbound verification and policy layer: identity, principal, declared purpose, requested action, mandate, policy scope, risk signals and policy response.
Inputs the alpha is built around
Alpha onboarding starts with the sources that matter to the prediction unit. The platform does not need every system on day one; it needs enough rights-cleared historical data to validate labels, support, calibration and one decision loop.
Meta, Google, TikTok, affiliates, lifecycle, campaign exports, creative performance.
CRM, pipeline stages, account ownership, opportunity movement, lead routing, sales capacity.
Shopify or commerce tables, orders, products, stock, margin, returns, discount pressure.
Warehouse tables, CSV, server-side event batches, first-party web events, reporting exports.
Report generation, pricing requests, stock checks, routing requests, checkout or workflow actions.
Security, privacy, and governance
Levered is designed for teams that need to trust the decision path. The platform keeps data policy, dataset rights, model versions, policy scopes, forecasts, decisions and audits visible enough for operators and leadership to inspect.
Levered is designed around customer-owned data: events, CRM, commerce, campaign, warehouse and operational sources. No cross-site tracking and no data resale.
Customer data is scoped to workspaces. Recommendations, goals, credentials, and agent policies are kept within the operating boundary.
Each dataset needs documented ownership, licence, permitted purpose and retention terms before training or commercial model use.
Forecasts, decisions, agent requests, policy outcomes, user responses, actions and causal audits are logged with versioned context.
Model versions, feature schemas, forecast logs, calibration checks, champion/challenger evaluation and drift-triggered recalibration are first-class.
Weak support widens intervals, backs off to broader segments, or returns insufficient evidence instead of false confidence.