Dweve Mesh: Your AI network that actually works
Transform idle European devices into distributed inference infrastructure through edge computing. Earn tokens by sharing compute, use tokens for AI services. Peer-to-peer AI with 96% less energy, complete privacy, and European data sovereignty.
We get your frustration
You're tired of expensive AI that doesn't work when you need it most. Cloud services that slow down when everyone's using them, costs that spiral out of control, and zero control over your own data. You want AI that's reliable, affordable, and actually yours.
Expensive and Unreliable
Cloud AI gets expensive fast and slows down when you need it most. You pay for peak usage but get inconsistent performance when demand is high.
No Control Over Your Data
Your sensitive business data gets sent to someone else's servers. You have no idea where it goes, who can access it, or how long they keep it.
Vendor Lock-in Trap
Once you build on a cloud AI platform, you're stuck. Switching providers means rebuilding everything from scratch, so they can raise prices whenever they want.
Sovereign AI computing built on edge devices
Turn 10-16 hours of daily idle computer time into productive edge computing infrastructure. Earn tokens contributing compute, spend tokens using peer-to-peer AI services.
Most EU datacentres are owned by American and Chinese companies. But Europe has more home and office devices than any continent. These machines sit idle 10-16 hours daily, wasting electricity. Dweve Mesh transforms this waste into sovereign AI computing infrastructure through peer-to-peer edge computing.
Hybrid privacy: your private data stays local, general AI tasks distribute across the peer-to-peer mesh. Install native for best performance, or contribute through your browser with WebAssembly. Simple token credits (1 token = 1 word) for transparent pricing.
Edge computing efficiency
Leverage idle edge computing capacity across Europe. Transform wasted electricity into productive peer-to-peer AI infrastructure through distributed inference.
Hybrid privacy
Private data stays local, never entering the mesh. Non-private computation distributes for optimal performance. GDPR compliance through technical architecture and European data sovereignty.
Sovereign AI computing
Compete with US and Chinese datacentre infrastructure through Europe's distributed edge computing advantage. Data processed on European hardware under EU jurisdiction.
Key technical advantages
Efficient distributed inference
Low-bit operations (1-8 bits) reduce computational overhead per operation, enabling efficient distributed inference on standard edge computing hardware.
Compact models
Discrete representations dramatically reduce memory requirements, allowing larger models to run on consumer edge computing hardware.
Low power consumption
96% less energy per operation compared to traditional AI, making idle edge computing device participation sustainable.
Broad hardware support
Optimised implementations for CPU, GPU, FPGA, and NPU enable peer-to-peer AI participation across diverse edge computing configurations.
Sophisticated 3-tier peer-to-peer network design
Optimised for distributed inference and resilience across edge computing, compute, and coordination layers
Global peer-to-peer network orchestration & consensus
High-performance distributed inference computing
Local & distributed inference on edge computing
Tier specialisation & roles
Coordination tier functions
Manages global peer-to-peer network state and maintains consensus.
- Network topology optimisation and routing
- Global load balancing across distributed edge computing nodes
- Byzantine fault-tolerant consensus mechanisms
Compute tier functions
Handles intensive distributed inference and federated learning.
- Complex distributed inference with 10 to 20 times speedup
- Decentralised federated learning with encrypted updates
- 96% less energy through low-bit operations (1-8 bits)
Architecture advantages
Edge tier functions
Local distributed inference on edge computing contributor devices during idle periods.
- Desktop and home edge computing hardware (idle 10-16 hours daily)
- Native installation or WebAssembly browser contribution
- Universities earn discounts by contributing edge computing capacity
Key architecture benefits
Lightning-fast Distributed Inference
Parallel processing across distributed edge computing nodes for fast, scalable peer-to-peer AI performance
How distributed inference works
Input decomposition
Automatically decompose queries into parallelisable sub-tasks for efficient distribution across the peer-to-peer network.
Parallel execution
Distribute sub-tasks with optimal routing based on edge computing node capabilities, location, and current load.
Result aggregation
Merge partial results using consensus algorithms to produce coherent, comprehensive solutions.
Performance advantages
Discrete AI architecture (1-8 bits) delivers efficient distributed inference performance across idle European edge computing devices.
Efficient operations
Low-bit operations reduce computational overhead, enabling efficient distributed inference across edge computing.
Scalable capacity
Peer-to-peer network capacity scales dynamically with participating edge computing devices across Europe.
Sustainable processing
96% less energy per operation enables sustainable peer-to-peer AI on idle consumer edge computing hardware.
Real-time processing
Low-latency distributed inference with parallel execution across the peer-to-peer mesh network.
Elastic scaling
Auto-scale distributed inference capacity based on demand with 96% less energy consumption.
Fault tolerance
Redundant processing ensures continuity when edge computing nodes fail or disconnect.
Privacy-first Federated Learning
Collaborative model training without data sharing through decentralised AI, preserving European data sovereignty while enabling collective intelligence
Privacy-preserving collaborative learning
Dweve Mesh enables multiple parties to collaboratively train AI models without sharing their raw data through decentralised federated learning. Using discrete AI (1-8 bit computation) with 10 to 20 times speedup and efficient low-bit encoding, this approach preserves European data sovereignty while enabling the benefits of large-scale, diverse training data.
Local training
Each participant trains the global model on their local edge computing data with efficient low-bit operations, computing gradients without sharing raw information while using 96% less energy.
Secure aggregation
Model updates are encrypted and aggregated using advanced cryptographic protocols that preserve individual privacy and European data sovereignty.
Global model update
The improved global model is distributed back to all participants through peer-to-peer network, benefiting from collective learning insights while maintaining compact low-bit representations.
Key benefits
Complete privacy protection
Raw data never leaves its origin. Only encrypted model updates are shared through federated learning, ensuring complete European data sovereignty and GDPR compliance.
Superior model performance
Access to diverse, distributed datasets results in more robust and generalisable AI models through privacy-preserving federated learning.
Reduced infrastructure costs
Decentralised federated learning with efficient low-bit operations and 96% less energy consumption leverages idle edge computing hardware instead of requiring massive datacentre investments.
Key application areas
Cross-hospital medical research through federated learning without sharing patient data
Fraud detection across institutions through federated learning while preserving customer privacy
Enterprise-grade Privacy & GDPR compliance
Built-in privacy protection and European data sovereignty for the most demanding enterprise environments
Privacy by design architecture
Dweve Mesh implements hybrid privacy protection at the architectural level through decentralised AI: private data stays exclusively local on edge computing devices, while non-private computation distributes across the peer-to-peer mesh. Privacy and European data sovereignty are fundamental to system operation, with 10 to 20 times speedup through efficient discrete operations.
Zero-knowledge processing
Advanced cryptographic protocols enable distributed inference on encrypted data without ever exposing the underlying information.
Differential privacy
Mathematical guarantees that individual data points cannot be identified from aggregate results in federated learning.
Complete GDPR compliance
Dweve Mesh provides comprehensive tools and mechanisms to ensure full compliance with GDPR and European data sovereignty regulations across all jurisdictions.
Right to information
Automated transparency reports show exactly how personal data is processed, stored, and used within the decentralised peer-to-peer mesh.
Right to data portability
Standardised export mechanisms allow users to retrieve their data in machine-readable formats.
Right to erasure
Secure deletion protocols ensure complete removal of personal data from all edge computing network nodes.
Simple token credits
Transparent usage-based pricing: earn tokens by contributing idle edge computing capacity, spend tokens on peer-to-peer AI services
Simple credit system
API tokens work like credits: 1 token equals 1 complete word (not subtokens like competitors). Earn tokens by contributing idle edge computing capacity to the public peer-to-peer mesh, spend tokens on distributed inference API usage. No trading, no blockchain, no cryptocurrency. Just transparent pricing sold exclusively by Dweve.
Earn tokens contributing edge computing
Transform idle edge computing hardware (10-16 hours daily) into productive sovereign AI computing capacity and earn API tokens for processing distributed inference requests.
- Contribute edge computing via native installation
- Contribute via WebAssembly in browser (no installation)
- Universities earn discounts on Dweve products
Spend tokens on peer-to-peer AI services
Use earned or purchased tokens for distributed inference API calls with transparent per-word pricing. Predictable costs, no hidden fees.
API usage credits
1 token = 1 complete word. Simple, transparent pricing for all peer-to-peer AI services across the Dweve platform.
Network participation
Token holders can participate in peer-to-peer network governance decisions and protocol improvements.
Industry applications
Transforming industries with decentralised AI infrastructure and sovereign AI computing
Healthcare
Privacy-preserving medical research and collaboration across institutions without sharing sensitive patient data through federated learning.
- • Cross-hospital medical research with federated learning
- • Decentralised federated model training
- • Patient privacy protection with European data sovereignty
- • Distributed inference diagnostic systems
Financial services
Collaborative fraud detection and risk modelling through federated learning while maintaining strict privacy and European data sovereignty.
- • Cross-institution fraud detection with federated learning
- • Distributed inference risk modelling
- • European data sovereignty compliance
- • Customer privacy protection through decentralised AI
Public sector
Secure collaboration between government agencies and research institutions through decentralised AI while maintaining European data sovereignty.
- • Cross-agency data analysis with federated learning
- • Privacy-preserving research through peer-to-peer AI
- • Citizen data protection with European data sovereignty
- • Sovereign AI computing compliance
Let's have a real chat
No sales robots, no automated responses: just real European AI experts who understand your challenges and actually want to help you succeed.
Ready to talk?
Whether you want to explore the technology, discuss a specific use case, or join our selective onboarding program, we're here to help.