Multi-Agent AI System

AI That Learns What Works
Across Your Entire Brand

Autonomous marketing optimization that discovers winning strategies through structured experiments. Each test makes every product smarter through cross-product learning and hierarchical beliefs.

See How It Works

Your Marketing Knowledge
Dies With Every Campaign

"Every product starts from scratch"

No learning transfer between products. What you discovered about Product A never helps Product B.

"What worked stops working"

No systematic belief decay or refresh. You're running last quarter's playbook in this quarter's market.

"Knowledge walks out the door"

Your best marketer leaves, insights vanish. Nothing captured in a system that persists.

"Testing is expensive guessing"

No quality validation on hypotheses. You're spending budget testing bad ideas.

A Scientific System,
Not a Black Box

Four-phase learning cycle with multi-agent architecture and adversarial critics validating every output.

PHASE 1

EXPLORE

Strategy Agent

Analyzes brand DNA, audience DNA, and belief system to generate testable hypotheses. Adversarial critics score each hypothesis—weak ones get rejected before testing.

PHASE 2

TEST

Creative Agent

Creates ad variants that isolate one variable. Alignment critics verify proper A/B structure. Human publishes to ad platform with confidence the test is well-designed.

PHASE 3

ANALYZE

Analyst Agent

Observes metrics with statistical gates (min 48hrs, 1K impressions). Determines winner with confidence scoring. Proposes data-backed conclusions.

PHASE 4

LEARN

Belief Intelligence Agent

Extracts insights from results. Updates confidence using Bayesian methods. Promotes strong beliefs up the hierarchy. Shares insights with sibling products.

Key Innovation: Every agent has adversarial critics that challenge its outputs. Bad hypotheses get rejected before you spend a dollar testing them.

Marketing Knowledge
That Compounds

Hierarchical belief system captures what works, promotes strong insights, and shares learning across products.

BRAND LEVEL
"Authenticity messaging outperforms hype across all products"
Confidence: 0.87 Evidence: 12 experiments
AUDIENCE LEVEL (Women 25-34, Health-Conscious)
"Before/after transformations drive 34% higher CTR"
Confidence: 0.72 Evidence: 5 experiments
PRODUCT LEVEL (Fitness Tracker Pro)
"Heart rate accuracy > step counting in messaging"
Confidence: 0.65 Evidence: 2 experiments
// How Beliefs Propagate
Promote: Product belief validated 3+ times → moves to Audience level
Share: Sibling products automatically test audience-level beliefs
Decay: Beliefs lose confidence over time without new evidence
Commit-Immediately: Human reviews AI decisions, can rollback with feedback

Every Product Makes
Every Product Smarter

When products share audiences, insights automatically propagate. Launch a new product and it inherits everything your brand has learned.

// Example: Cross-Product Learning
Product A: Fitness Tracker
├─ Tests "pain-focused messaging"
├─ Wins with +28% CTR
└─ Insight promoted to Audience level (Women 25-34, Health-Conscious)
Product B: Yoga Mat (Same audience)
├─ Automatically receives insight as priority hypothesis
├─ Tests "pain-focused messaging" for yoga context
└─ Validates with +31% CTR
Product C: Protein Powder (Different audience)
└─ Does NOT inherit the belief
(Learns independently for its audience)

The Advantage: Launch a new product and it starts with everything your brand has already learned—but only for overlapping audiences.

Not Just AI.
A Learning Organization.

Multi-Agent Architecture

Strategy, Creative, Analyst, and Belief Intelligence agents work together with adversarial critics validating outputs. Not a single model—a system of specialized agents.

🔄

Adversarial Quality System

Every hypothesis scored by critics. Every variant validated for proper A/B structure. Bad ideas rejected before spending budget. Multi-phase quality gates ensure rigor.

🧠

Hierarchical Beliefs

Three-tier knowledge system (Brand → Audience → Product) with automatic promotion/demotion. Bayesian confidence updates. Time-based decay for freshness.

🔗

Cross-Product Learning

Sibling products sharing audiences automatically exchange insights. What Product A learns, Product B can test. Portfolio-wide intelligence.

📊

Statistical Rigor

Observation gates (min 48hrs, 1K impressions). Bayesian confidence updating. Decay functions for belief freshness. Scientific methodology, not gut feeling.

↩️

Human-in-the-Loop

AI commits decisions immediately, emails you notifications. Review at your pace. Rollback with feedback if needed. System learns from corrections.

Join the Early Access Program

Shape the future of autonomous marketing. Full system access, email support, and founding pricing for early partners.