Content Safety Models That
Get Smarter Together
Enterprise content moderation. Train on your infrastructure, access foundational models trained across the industry, benefit from network effects as the cooperative grows. Patent protection included.
You Need Patent Licensing. Here's The Smart Way
Modern content moderation systems use AI-powered inspection, user-specific restrictions, and real-time decisions. These architectures may practice our granted patents (WO2022137077A1, granted in EU, UK, South Africa; US pending).
If you're running content moderation at scale, you likely need a patent license. We offer two paths forward.
- ✓ Dual-factor AI moderation (rating + confidence scoring)
- ✓ Real-time visual content inspection
- ✓ User-specific restriction parameters
- ✓ Selective content redaction based on AI analysis
- 15+ jurisdictions granted
- EU, UK, South Africa
- US Patent Pending
- Priority Date: December 2020
Two Ways to License
Both options provide full patent protection. Cooperative membership adds foundational models, infrastructure flexibility, and expert access—at 50% lower cost.
Standalone License
starting at
scales to £1M+ for enterprise
What's Included:
- ✓Patent license protection
- ✓Compliance certificate
What's NOT Included:
- ✗Models or AI infrastructure
- ✗Technical support
- ✗Ongoing improvements
- ✗Cooperative benefits
You'll Also Need:
- •Dedicated ML engineering team
- •Infrastructure & cloud services
- •Continuous R&D investment
Cooperative Membership
starting at
scales to £500K+ for enterprise
50% savings at every tier
Everything Included:
- ✓Patent license + compliance
- ✓Access to foundational models
- ✓Federated learning infrastructure
- ✓Monthly model updates
- ✓Integration support
- ✓Network effects benefits
How Training Works:
- ✓Train on your servers with your data
- ✓Share only encrypted model weights
- ✓Your data never leaves your infrastructure
- ✓You own your models - no lock-in
Founding member rate - locked in for 3 years
Both options provide patent licensing. Cooperative membership adds model access, infrastructure, and ongoing support—at 50% lower cost than standalone licensing alone.
How Cooperation Creates Value for Everyone
Your participation improves models for the entire network—including yours. Cooperative economics mean better models at lower cost than any platform could achieve alone.
How It Works
You Train Locally
Use your own data on your servers. Your content never leaves your infrastructure.
Share Encrypted Weights
Contribute model parameters only (not data). Differential privacy protects individual examples.
We Aggregate
Combine weights from all members using secure aggregation protocols.
Everyone Benefits
Receive improved models trained across diverse data you couldn't access alone.
The Cooperative Flywheel
Hyperscalers Contribute
Global data diversity, billions of edge cases, multi-language training, regulatory compliance experience.
→ Improves foundational models for everyone
Regional Platforms Add
Local context, cultural nuances, language-specific patterns, niche content types.
→ Models work better across cultures
Our Expert Team Provides
Specialized ML expertise, custom model training, regulatory compliance guidance, continuous optimization.
→ Every member benefits from expert support
Everyone Benefits
Better accuracy, comprehensive coverage, regulatory compliance, continuous innovation, lower costs.
→ Industry-wide improvement
No Single Platform Could Build This Alone
Hyperscalers need global diversity. Regional platforms need scale. Startups need accessibility. Model trainers need data. Together, we create industry-leading models that serve everyone.
Clear, Predictable Costs
Transparent annual pricing from startups to enterprise platforms. All memberships include patent licensing, foundational model access, and cooperative participation. Founding members get locked in rates.
⭐ Founding Member Pricing
First 20 members get exclusive rates: £12K-£500K/year locked in. After 20 members, standard rates apply. This is a limited-time opportunity to establish founding member economics.
| Platform Size | Standalone License | Cooperative Member | Annual Savings |
|---|---|---|---|
| Startup <1M users | £25K | £12K | £13K (50%) |
| Growth 1-10M users | £100K | £50K | £50K (50%) |
| Mid-Market 10-100M users | £400K | £200K | £200K (50%) |
| Enterprise 100-500M users | £1M | £500K | £500K (50%) |
Platforms serving 500M+ users: Custom enterprise agreements available. Contact us for board-level negotiations including governance participation and multi-jurisdiction coverage.
Pricing Based On
Annual content volume (images, videos, text), active users, API request volume. Custom pricing available for unique situations.
What's Included
Full patent license, federated learning infrastructure, foundational models, monthly updates, integration support, compliance docs, governance (Enterprise+).
Volume Discounts
Multi-year commitments, early adopter rates for founding members, enterprise custom agreements.
Why Every Platform Benefits
The cooperative creates value across the entire industry. Network effects improve models for everyone—from startups to hyperscalers.
Building the Network
As the Network Grows
Why Federated Learning Matters
Traditional Centralized Training
- ✗ All partners send raw data to central server
- ✗ Massive GDPR and privacy concerns
- ✗ Competitive data exposure
- ✗ Single point of security failure
- ✗ Platforms will never participate
Our Federated Approach
- ✓ Train on your servers with your data
- ✓ Share only encrypted model weights
- ✓ No competitive exposure
- ✓ GDPR compliant by design
- ✓ Better models through collaboration
Enterprise-Grade Infrastructure
Our federated learning platform provides production-ready infrastructure for platforms at any scale.
Privacy & Security
Differential Privacy
ε=1.0, δ=10⁻⁵ guarantees protect individual examples
Secure Aggregation
Encrypted weight transmission using secure multi-party computation
Compliance
SOC 2 Type II certified • GDPR compliant by design • DSA/OSA ready
Integration
APIs & SDKs
REST + gRPC APIs • Python, JavaScript, Java, Go SDKs
Deployment
Docker containers • Kubernetes-ready • Average integration: 2-4 weeks
Documentation
Complete integration guides • Sample code • Technical support
Model Performance
Latency
<30ms inference • <10ms for cached results
Availability
99.9% uptime SLA • Global edge deployment
Scale
Handles Billions of requests/day • Auto-scaling infrastructure
Accuracy
92%+ on standard benchmarks • Improves with network growth
Technical Deep Dive Available: Schedule a discovery call for detailed architecture review, security audit results, and integration planning with our engineering team.
Simple Onboarding
From first call to production integration in 4-6 weeks. We make joining the cooperative straightforward.
30 minutes
- • Discuss your moderation needs
- • Review current implementation
- • Assess patent licensing requirements
- • Answer technical questions
1-2 weeks
- • Sign membership agreement
- • Finalize patent licensing terms
- • Define integration scope
- • Establish governance rights
2-4 weeks
- • Integrate federated learning SDK
- • Connect to model serving
- • Train initial models on your data
- • Go live with production traffic
Ready to discuss your platform's needs?
Common Questions
Clear answers to help you understand our cooperative model and patent licensing.
What exactly does the patent cover?
Our patents cover AI-based content moderation systems that use dual-factor decisions (rating + confidence level), real-time visual content inspection, user-specific restriction parameters, and selective content redaction. See our patent documentation for detailed claims.
How do I know if I need a license?
If you're using AI to inspect content in real-time, make moderation decisions based on multiple factors, and apply user-specific restrictions, you likely practice our patent claims. We can review your implementation during a discovery call.
Why is cooperative membership cheaper than standalone licensing?
Members contribute to federated learning, creating value for the entire network. We share those economics through reduced rates. You also avoid building infrastructure yourself.
What data do I have to share?
You share encrypted model weights only—never raw data, content, or competitive information. Your training data stays on your servers. Federated learning means privacy by design.
What if I want to leave the cooperative?
Members can exit. You keep your trained models but lose access to future updates and must negotiate standalone patent licensing.
Do I have to contribute to federated learning?
Active participation (sharing weights) is required for cooperative membership. If you want patent license only, choose standalone licensing option.
How many members are there currently?
We're onboarding founding members across all tiers—from startups to hyperscalers. First 20 members get locked-in pricing and governance seats. Target: 20+ platforms in 18 months to achieve network effects.
Do I train on my own infrastructure?
Yes. Federated learning means you train locally on your servers—your data never leaves your infrastructure. Only encrypted model weights are shared with the cooperative. This is how we maintain privacy while enabling collective intelligence. We provide SDKs, integration support, and initial model weights to get you started.
Can I see the code for federated learning infrastructure?
We provide technical documentation and integration specs. Core infrastructure is proprietary but auditable for security/privacy verification.
Is this compliant with EU regulations?
Yes. Federated learning is GDPR-compliant by design (no data transfer). Our architecture supports DSA and Online Safety Act requirements. We provide compliance documentation.
Still have questions?
Schedule a 30-minute discovery call to discuss your specific situation.
Schedule Discovery Call