Frank Kern
RETAINER CLIENT · WIRED FOUR MORE TIMES SINCE
$41,253
NEW PROFIT · 3 DAYS
System: Healthy
The loop that actually closes
Goal in. Proof out. The next loop starts smarter.
Six stages — Sense, Understand, Plan, Execute, Measure, Learn — running continuously against your real data. Every stage governed. Every run audited. Every result stored so next week's plan already knows what worked.
The same loop Jon built by hand for Hormozi, Kern and Crestani — productized and running.
6 stagesFully governedCausal proofCompounding weekly
What each loop earns you.
6
Stages, zero hand-offs
Sense → Understand → Plan → Execute → Measure → Learn
11h
Reclaimed per week
Median, across operators running ≥4 loops
< 0.3
Auto-rollback threshold
Losing runs killed before next Monday
∞
Loops per year
Bounded only by what is worth doing
The six stages. One continuous loop.
Every stage feeds the next. Break one and the compounding stops. Ship all six and the OS gets measurably better every week without you tuning it.
Sense
Every connected tool, every webhook, every event streamed into one real-time feed. The OS sees the $6,200 losing ad set on day two — not when accounting closes.
Understand
Signals become a live model of your business — margins, cohorts, constraints, policies. Not a dashboard: a simulation you can ask questions against before you act.
Plan
You type the goal. The Plan Engine writes a sequenced execution plan with dependencies, approval gates, and spend caps — all policy-validated before a single action queues.
Execute
148 governed tool handlers fire across 10 OAuth providers. Every action logged with reason-why, timestamp, policy reference, and a rollback receipt where possible.
Measure
Proof Ledger captures causal impact — not clicks. Control vs treatment. Baseline vs outcome. The attribution data that survives a finance audit.
Learn
Winning runs get promoted to workspace-scoped playbooks. Losing runs get killed (auto-rollback at score < 0.3) and annotated. Next week starts with what already worked at your business.
Stage 1 — Sense.
Reality, continuously. Not a daily digest, not a weekly export. Every webhook, every event, unified before your team has coffee.
Ad performance
ROAS, CPM, CTR, frequency, cohort-level net profit across Meta, Google, TikTok, LinkedIn — streamed, not scraped.
Commerce
Orders, refunds, AOV, subscription churn, stockout risk — from Shopify, WooCommerce, BigCommerce.
CRM + pipeline
Deal stage, MRR motion, churn risk, product-usage signal — from HubSpot, Salesforce, Pipedrive.
Support + voice-of-customer
Ticket volume, CSAT deltas, SLA breaches, topic clusters — from Zendesk, Intercom, Freshdesk.
Example prompt
What changed in revenue yesterday by channel?Signals fused: $84K spend, $212K revenue, 18 deals closed — all channels reconciled.
Spike isolated: paid-social retargeting — ROAS 1.8× the 7-day average.
Anomaly: email CTR -22%, opens flat — classic creative fatigue signature.
Queued: review email variants + proof snapshot scheduled for 48hr follow-up.
Stage 2 — Understand.
Signals become insight you can act on. Not "revenue is down" — exactly which SKU, in which channel, against which cohort, breaking which constraint.
Impact modeling
Which levers would actually move this week's target given your current margin, spend, and capacity.
Constraint mapping
Margin floors, spend caps, approval thresholds — factored before the Plan Engine runs.
Dependency graph
What needs to happen in sequence vs parallel. No wasted motion. No missed dependency.
Root cause isolation
Down to the SKU, segment, creative, or cohort. The "why" behind the dashboard line.
Example prompt
Why did margin dip 3 points while ROAS held last week?Root cause: shipping-cost surge on 2 SKUs running in retargeting.
Secondary: promo stack overlap — two discount codes applied to the same cohort.
Constraint flagged: margin floor (22%) will breach by Thursday if unaddressed.
Recommendation: pause retargeting for affected SKUs; fix promo exclusion rules.
Stage 3 — Plan.
Goals become governed execution plans with dependencies, owners, approval gates, and success criteria. Every plan passes policy before a single action queues.
Structured sequences
Not a list of to-dos. A plan with dependencies, rollback checkpoints, and an expected-impact projection per action.
Policy validation
Every action checked against your spend caps, margin floors, and approval rules before queueing.
Approval gates
High-risk and high-value actions routed for human review. Everything else runs automatically within policy.
Experiments baked in
Control groups, variant design, and proof setup are part of the plan — not a separate BI build.
Example prompt
Plan to lift profit 8% this month without new ad spend.Reallocate $18K from low-margin cohorts to high-margin, inside existing budget.
Action 1: throttle 3 SKUs < 20% margin; pause their retargeting segments.
Action 2: lifecycle sequence for 1,400 repeat buyers; no discount.
Policy: 22% margin floor enforced; approval required for any spend shift > $5K.
Proof: baseline captured; impact snapshot in 14 days.
Stage 4 — Execute.
Approved plans fire across your stack. 148 governed tool handlers, 10 unified OAuth providers, one immutable audit trail.
Cross-stack runs
Meta Ads, Google Ads, Klaviyo, HubSpot, Shopify — one governed workflow, zero glue code.
Spend ledger
Per-action caps, daily limits, monthly limits — enforced at the Execution Layer. No double-spend, no runaway.
Live run monitor
Watch runs execute in real time. Pause, modify, or kill any automation from one view.
Immutable audit
Every action: who triggered it, what policy allowed it, what data it touched, where the rollback receipt lives.
Example prompt
Execute the profit plan. Enforce every policy.Ad reallocations queued in Meta + Google — approval gate cleared.
Lifecycle run: 1,400 contacts sequenced in Klaviyo — trigger: 2+ purchases.
Throttle applied: 3 SKUs removed from retargeting; shopping campaigns paused.
Spend cap active: $5K threshold — any override requires VP approval.
Audit: every action logged with timestamp, policy reference, data source.
Stage 5 — Measure.
Not "engagement went up." Causal, net-profit attribution with cannibalization and overlap netted out. Proof that survives a board review.
Causal attribution
Control vs treatment, baseline vs outcome, statistical confidence on every run.
Experiment results
Lift measurement per variant, confidence interval, effect decay curve.
Impact snapshots
One-click proof export for investors, board, clients — formatted the way finance already reads it.
Trend detection
Early-warning signals before anything hits the P&L line.
Example prompt
Show the proof for the profit run after 14 days.Incremental profit lift: +6.8% vs control (goal was 8%).
Primary driver: ad reallocation — margin-adjusted ROAS up 1.4×.
Secondary driver: lifecycle sequence — repeat-rate +11% in treated cohort.
Attribution confidence: 91% (causal, not correlational).
Learning saved to memory: "margin-adjusted reallocation > volume targeting for this workspace."
Stage 6 — Learn.
Every result — win or loss — goes into workspace-scoped Business Memory. Next week's plan starts with what already worked on your data, not a generic model.
Playbooks, not lessons
Winning runs saved as reusable, parameterized playbooks. When the pattern recurs, the OS already knows what to queue.
Policy refinement
Edge cases and near-misses become new guardrails. The OS gets safer with every loop.
Trigger rules
Condition-driven playbooks. The loop runs unattended while you approve exceptions.
Compounding returns
Operators past loop 50 report 2-3× better outcomes than their first ten. The OS literally improves.
Example prompt
Save this profit run as a reusable playbook.Playbook saved: "Margin Recovery — Retargeting Cleanup."
Trigger: margin-floor-breach signal OR retargeting ROAS < 1.2× for 3+ days.
Actions: throttle low-margin SKUs, reallocate to proven cohorts, launch lifecycle.
Policy: 22% floor; $5K approval gate; proof snapshot at day 14.
Status: active — next matching trigger will auto-queue the plan for your review.
Goal to proof. In one continuous loop.
Goal becomes plan. Plan becomes run. Run becomes proof. Proof becomes learning. Learning makes the next loop start smarter. That compounding is the whole product.
- Goal → Plan
- Plan → Run
- Run → Proof
- Proof → Learning
- Learning → Better next loop
Run the loop on your real data.
Free trial, no card, first loop running inside 15 minutes. Or book 30 minutes and watch Jon plug it into your stack while you ask questions.
What this has already been worth.
AI Acquisition
SIGNED PARTNERSHIP · THE GROWTH PARTNER DMCC, DUBAI
$70,000,000
PER YEAR · RUNS ON THIS
Greg Chambers
CEO, TRAFFIC AUTHORITY · ON TRAFFIC HE WAS ALREADY BUYING
$148,368
ADDITIONAL PROFIT
Every figure is a payment that cleared.