Marketing Operating System

What if every output was an experiment that made the next one better?

MH-OS is a closed-loop marketing system. Intelligence sees. Strategy decides. Execution acts. And compounding loops make sure week 50 is better than week 1.

27
Automated
intelligence tasks
10
Execution
modules
9
Expert
evaluators
12
Compounding
loops
Typical marketing team
5-10
experiments / quarter
vs
MH-OS at maturity
560-840
experiments / quarter

The difference isn't effort. It's architecture.

Act 1

See Everything, Miss Nothing

27 automated tasks analyze every channel — ads, funnel, churn, competitors, website conversion — and generate intelligence every day, week, and month.

Revenue Driver Tree — 7 Levers

Every metric that feeds revenue, mapped and measured
#1 Reduce Churn
$450K/mo walking out the door
$2.7M/yr
#2 Fix Deal Funnel
40% drop-off at Offered→Intro
$1.9M/yr
#3 Build Organic Channels
100% paid dependency, FF down 47%
$2.5M/yr
#4 Lifecycle / Nearbound
1,356 boomerang customers, $0 marketing
$3-5M/yr
#5 LinkedIn Content
Zero organic social since 2023
Brand
#6 Channel Reallocation
Google 3.5x more efficient than FB
$1.5-2.5M
#7 Scale AI Segment
15 companies today at 3.4x ARPC, growing 7.7x/year
$19M ARR

Weekly Intelligence — March 16, 2026

Form Fills
172
Deals Signed
16 +45% WoW
Weekly Spend
$103K
Cost / Appt
$2,522 +27%
SYSTEM-DETECTED INSIGHT

Facebook is burning $70K for 16 appointments at $4,378 CPA while Google delivers 12 appointments at $2,090 CPA. The system caught this automatically — no one had to check a dashboard.

Wow Moment The system doesn't just report — it proposes experiments

Facebook Budget Reallocation
Low Effort Test This Week
Observation: FB CPA ($4,378) is 2.1x higher than Google ($2,090)
Hypothesis: Shift $20K/week from Facebook to Google → Cost/Appt decreases 15-20%
Outcome: Human approves. System executes. Results feed next week's signal.
Freelancer Supply Sprint
High Effort Next Sprint
Observation: Freelancer offers dropped 64% while QTB crashed 56%
Hypothesis: Fast-track 20 new onboardings → QTB increases 30-40%
Outcome: Supply constraint identified as conversion bottleneck, not marketing.
Act 2

10 Modules, One System

Creative, lifecycle, outbound, sales, ads, SEO, newsletters, LinkedIn — every channel has a dedicated execution module with shared intelligence.

Creative 9 agents, 20 commands
Lifecycle 3 agents, 3 commands
Outbound 5 commands
Google Ads 5 agents, 7 skills
Product 5 agents, 12 skills
Sales 2 commands
SEO 12-step pipeline
AEO AI citation audit
Newsletter Signal-driven
LinkedIn Founder voice

See the outputs

Wow Moment Lifecycle Module

14-Dimension Lifecycle Audit

Scored MarketerHire's HubSpot across 14 dimensions — workflows, funnel conversion, retention, expansion, resurrection. Queried 164 BigQuery tables, 162K deals, 950K contacts.

0.33
Health Score
$5.5M
Expansion Opportunity
62.3%
No-show losses
See Audit Live → 30/60/90 Plan →

A consultant takes 2-4 weeks. The system did this in a single session.

Sales Module

Account Enrichment + Sales Deck

One enrichment run produces three outputs: internal research dossier, external sales deck, and ABM landing page. Company intel, deal history, decision makers, marketing audit, competitive landscape.

Firmographics
Traffic Data
CRM History
Competitive Intel
See Sales Deck →

One enrichment, three outputs. All from the same data.

Wow Moment Creative Module

Production Landing Pages in 30 Minutes

Zero-dependency HTML. Fully branded with design system compliance. Deployed to a custom Vercel subdomain. Two brand variants — MarketerHire (light, professional) and MH-1 (dark, technical) — from the same system.

Traditional
3-5 days
MH-OS
30 minutes
DESIGN SYSTEM
Extracted from live marketerhire.com. Typography, color tokens, component patterns, responsive specs, both brand variants — all codified.
QUALITY GATE
Writing QA scans for AI tells. Voice compliance checks against banned patterns. Brand identity verified against canonical guide.
Act 3

Every Output Is an Experiment

The real power isn't any single output. It's that every output makes the next one better. Compounding loops turn one-shot generation into persistent improvement.

4-Layer Architecture

PROGRAM Human direction — never auto-modified
lever-priorities.md · revenue-model.md
EDIT One change per loop cycle
Ad copy, email subjects, landing pages, bid strategies
EVALUATE Hard metrics — no ambiguity
CPA, CTR, reply rate, churn rate, win rate
KNOWLEDGE What compounds across runs
playbooks/, signals/, Airtable, Supabase
Wow Moment

One signal improves three channels

SIGNAL SOURCE: Sales Call Transcript

"We need someone who understands AI marketing, not just traditional digital."

AD CREATIVE LOOP

Generates variant: "Your AI startup deserves an AI marketing team"

3.1% CTR vs 1.8% baseline
OUTBOUND LOOP

Same "AI marketing" framing in cold email subject lines

↑ Reply rate
LIFECYCLE LOOP

Winning hook becomes email subject for re-engagement sequence

↑ Open rate

Each loop learns. Each loop feeds the others. Run 1 is a guess. Run 100 is a knowledge base.

9-Expert Evaluation Panel

Before anything ships, the system can run it through a panel of experts with quantified rubrics from their actual published frameworks. Auto-selected 3-5 per task type. Parallel scoring, then a debate layer resolves disagreements.

Schwartz Persuasion
Ogilvy Advertising
Dunford Positioning
Wiebe Conversion
Kramer B2B GTM
Cialdini Influence
Cabane Growth
Kaushik Analytics
Gerhardt B2B Brand
Impact

What This Means in Dollar Terms

Every lever is quantified. Every scenario is modeled. The system maps execution to revenue impact.

Scenario Active Clients Gross ARR Key Moves
Do nothing 275 → declining $27M → $13M
Churn fix only 400 $39M Retention 85% → 93%
Churn + acquisition 550 $53.6M + Scale organic, stabilize form fills
+ AI segment 550 core + 100 AI $84M AI @ 3.4x ARPC is the multiplier
Full execution 600 core + 120 AI $99.5M All 7 levers firing

The system doesn't replace your team. It gives your 3-person marketing team the analytical depth of 15 and the experiment velocity of a growth lab.

Build vs. Buy

You could build this. The architecture is documented. But it took 6 months of iteration to get to 27 automated intelligence tasks, 10 coordinated execution modules, and the compounding loop infrastructure.

The question isn't whether this is possible. It's whether you want to spend 6-12 months building it, or start running experiments next week.

27
Intelligence tasks
10
Execution modules
249
Framework files
579
Growth tactics
Book a Walkthrough

30-minute deep dive. Bring your team.