CreatorGraph is an automated brand-to-creator deal generation layer designed to integrate directly into creator ecosystems like Stan.
It transforms brand onboarding, campaign creation, creator matching, and outreach into a structured, intelligence-driven revenue pipeline.
Most creator platforms focus on storefronts, link-in-bio monetization, and inbound discovery.
CreatorGraph focuses on the missing side:
Turning creator ecosystems into structured, automated partnership engines.
Instead of:
- Creators manually searching for deals
- Brands manually browsing creators
- Cold outbound guessing
CreatorGraph:
- Structures brand intent
- Structures creator performance signals
- Computes compatibility deterministically
- Orchestrates deal flow automatically
The result is reduced friction, higher match quality, and scalable revenue generation.
CreatorGraph is not internal tooling. It is a platform layer embedded within the creator ecosystem.
- Brand pastes website URL
- AI agents crawl and build structured brand dossier
- Campaign briefs auto-generated
- High-fit creators ranked
- Outreach auto-generated (optional auto-send)
Brands can operate in:
• Auto Mode — fully automated campaign launch • Review Mode — approve matches and outreach • Manual Mode — custom selection and messaging
Creators do not search for gigs.
They receive:
- Ranked inbound deal opportunities
- Pre-qualified campaign briefs
- One-click accept/decline
Deals feel native inside the platform.
Brand URL
↓
Playwright Crawl Agent
↓
Structured Brand Dossier
↓
Brand Profiler (LLM)
↓
Postgres Knowledge Graph
↓
Compatibility Scoring Engine
↓
Ranked Creator Matches
↓
Outreach Agent
↓
Deal Lifecycle Tracking
CreatorGraph prioritizes measurable performance signals over subjective labels.
Creators are modeled using:
- niche_primary
- platforms
- followers per platform
- average views per platform
- engagement rate
- content formats
- top topics
- post frequency
This allows deterministic ranking and future ML optimization.
Preferred calculation:
engagement_rate = (avg_likes + avg_comments) / avg_views
MVP approximation:
engagement_rate ≈ avg_views / followers
Engagement is normalized to compare creators across audience sizes.
Each brand–creator pair receives a normalized score between 0 → 1.
niche_score = 1 if creator.niche_primary == brand.category else 0
topic_score = |intersection(creator.top_topics, brand.campaign_angles)|
/ |brand.campaign_angles|
platform_score = |intersection(creator.platforms, brand.preferred_platforms)|
/ |brand.preferred_platforms|
engagement_score = min(engagement_rate / target_rate, 1)
compatibility_score =
0.45 × niche_score +
0.35 × topic_score +
0.10 × platform_score +
0.10 × engagement_score
These weights are domain-informed priors and are designed to evolve into learned weights based on deal outcomes.
Uses Playwright to:
- Extract pricing
- Extract positioning
- Identify ICP
- Detect social presence
- Capture testimonials and case studies
Improves data quality for campaign generation.
Generates:
- Campaign angles
- Hook ideas
- Deliverables
- Suggested CTAs
- Measurement plan
Generates personalized messages grounded in:
- Brand dossier
- Creator signals
- Campaign brief
Match lifecycle states:
- suggested
- contacted
- replied
- interested
- closed
Outcome data enables:
- Weight optimization
- Conversion prediction
- Future learning-to-rank models
- Measurable over inferred
- Deterministic first, ML later
- Explainable scoring
- Automation by default, override optional
- Agent-enhanced data quality
- Creator enrichment agents
- Conversion prediction modeling
- Learning-to-rank optimization
- Multi-creator campaign optimization
- Full autonomous deal routing
CreatorGraph transforms creator ecosystems from monetization tools into revenue intelligence networks.
It bridges demand and supply using structured signals, automation, and agentic data acquisition — while preserving human control.
The result is scalable creator–brand commerce with reduced friction and higher conversion probability.