Business Model & Economics

Expert Fabric will operate as a cloud-based SaaS platform with multiple revenue streams designed to scale with usage and drive recurring revenue.

Revenue Model

Multi-Tier Pricing Strategy

1. Enterprise Subscriptions

• Target: Large organizations with consistent expert needs
• Structure: Monthly/annual subscriptions based on usage tiers
• Pricing: $10,000-$100,000+ annually
• Features:
  • Dedicated expert pools
  • Custom workflows and integrations
  • Advanced security and compliance
  • Priority support and SLAs

2. Transaction-Based Model

• Target: SMBs and ad-hoc users
• Structure: Platform commission on completed tasks
• Commission: 10-20% of task value
• Features:
  • Pay-per-use flexibility
  • Access to general expert pool
  • Standard quality assurance
  • Basic reporting and analytics

3. Hybrid Model

• Target: Mid-size businesses
• Structure: Base subscription + transaction fees
• Pricing: $1,000-$10,000 annual base + reduced commissions
• Features:
  • Guaranteed expert availability
  • Volume discounts
  • Extended feature access
  • Custom reporting

Supplementary Revenue Streams

Expert Node Marketplace

• Revenue share from third-party expert node developers
• Certification fees for premium expert nodes
• Template licensing for specialized workflows

Data & Analytics Services

• Market intelligence reports based on aggregated task data
• Benchmark analytics for industry performance metrics
• Custom research leveraging expert network

Professional Services

• Implementation consulting for enterprise deployments
• Custom expert node development
• Training and certification programs

Unit Economics

Core Metrics

Metric	Value	Notes
Average task revenue	$500	Blended across all customer segments
Average AI compute cost	$50	Including model inference and processing
Average human labor cost	$300	Expert compensation for review/enhancement
Platform commission (blended)	$50	10% average across pricing tiers
Gross margin	20%	($500 - $400) / $500

Cost Structure Breakdown

Direct Costs (80% of revenue)

• Expert compensation: 60% of task value
• AI compute costs: 10% of task value
• Infrastructure & operations: 10% of task value

Operating Expenses

• Sales & marketing: 30% of revenue
• R&D: 25% of revenue
• General & administrative: 15% of revenue

Scaling Economics

As the platform grows, unit economics improve through:

Network Effects

• Larger expert pool → Lower cost per expert hour
• More completed tasks → Better AI models → Lower AI costs
• Increased utilization → Better expert retention → Lower acquisition costs

Operational Leverage

• Infrastructure automation → Reduced operational costs
• Self-service features → Lower support costs per customer
• Platform efficiency → Higher gross margins

Financial Projections

Revenue Growth Trajectory

Year	Tasks/Month	Avg Task Value	Monthly Revenue	Annual Revenue
Y1	500	$500	$250K	$3M
Y2	2,000	$600	$1.2M	$14.4M
Y3	5,000	$700	$3.5M	$42M

Path to Profitability

Year 1: Foundation Building

• Revenue: $3M
• Gross margin: 20% ($600K)
• Operating expenses: $4M (investment phase)
• Net margin: -113% (building platform and network)

Year 2: Market Expansion

• Revenue: $14.4M
• Gross margin: 25% ($3.6M)
• Operating expenses: $8M
• Net margin: -31% (approaching breakeven)

Year 3: Profitable Growth

• Revenue: $42M
• Gross margin: 30% ($12.6M)
• Operating expenses: $15M
• Net margin: -6% (near profitability with scale)

Value Creation Strategy

For Clients

• Speed: Hours vs. days for complex deliverables
• Quality: AI efficiency + human expertise
• Cost: Optimized resource allocation reduces overall project costs
• Scalability: Access to global expert network on-demand

For Experts

• Flexible work: Choose projects and schedule
• AI augmentation: Focus on high-value activities while AI handles routine tasks
• Fair compensation: Transparent micro-payment system
• Career growth: Build reputation and specialization within network

For Platform

• Network effects: Value increases with more participants
• Data advantage: Accumulated knowledge improves service quality
• Platform leverage: Technology scales without proportional cost increases
• Market position: First-mover advantage in AI-human collaboration space

Economic Sustainability

Expert Retention Strategy

• Performance-based compensation: Higher pay for better quality and speed
• Career development: Training and upskilling opportunities
• Community building: Professional networking and collaboration
• Flexible engagement: Multiple ways to participate and contribute

Client Value Optimization

• Outcome focus: Pricing based on delivered value, not just time
• Continuous improvement: Platform learns and improves with each task
• Risk mitigation: Quality assurance and revision guarantees
• Strategic partnership: Long-term relationships vs. transactional interactions

Technology Investment

• R&D allocation: 25% of revenue invested in platform capabilities
• AI advancement: Continuous model improvement and optimization
• Infrastructure scaling: Proactive capacity planning and optimization
• Security & compliance: Enterprise-grade capabilities for market expansion

Competitive Economic Advantages

Cost Structure Benefits

• AI leverage: Reduces human labor requirements for routine tasks
• Network efficiency: Optimal expert matching reduces waste
• Knowledge reuse: Past work accelerates future deliverables
• Platform automation: Minimal marginal costs for additional transactions

Revenue Model Flexibility

• Multiple segments: Diversified customer base reduces risk
• Scalable pricing: Models adapt to customer size and needs
• Value-based pricing: Captured value scales with delivered outcomes
• Recurring revenue: Subscription components provide predictable income

This business model positions Expert Fabric to capture significant value in the growing AI-human collaboration market while building sustainable competitive advantages through network effects and operational leverage.