AI for Everyone.
Powerful federated learning made simple for small teams and organizations.
Federated Learning Made Simple
Perfect for startups, small teams, and organizations who want to harness the power of collaborative AI with simple, accessible tools.
Common Challenges Made Simple
Common hurdles that prevent small teams and organizations from leveraging collaborative AI and how NeuraFabric removes them effortlessly.
Sensitive Data Can't Be Shared
Your most valuable data - patient records, proprietary datasets, customer information can't be shared with collaborators due to privacy regulations and compliance requirements.
Security Feels Complex
Ensuring data security and compliance feels overwhelming for small teams. You need simple, built-in protections that don't require cybersecurity expertise or complex setup procedures.
Slow Results Frustrate Progress
Waiting for training jobs to complete, dealing with network issues, and restarting failed experiments wastes precious time and can derail critical project deadlines and milestones.
Technical Setup Is Overwhelming
Setting up distributed learning feels impossible without dedicated IT resources. You want to focus on your work, not wrestling with complex infrastructure and configuration files.
Distributed Intelligence
Small players across industries are using NeuraFabric's DNA to train better AI models together through federated learning - while keeping sensitive data completely private.
Open Data + Private Data Fusion
AI enthusiasts or small teams can combine open datasets with their own private data securely, improving accuracy without ever exposing what they want to keep hidden.
Community Energy Projects
Local energy groups or smart-home enthusiasts can collaborate to optimize power usage patterns across homes, while each household keeps its detailed data private.
Open Source AI Teams
Small distributed teams working on open-source AI can train models together across devices, without centralizing data, making projects more inclusive and secure.
Startup Collaboration on Limited Data
Small startups can pool model learnings together without ever sharing raw data. Each team keeps its data private but still benefits from stronger AI models trained on collective knowledge.
Researcher Collaboration
Independent researchers can work together by training models across their datasets without moving sensitive information. This enables collaborative projects that normally require big data access.
AI Enthusiast Communities
Hobbyists and AI learners can train shared models on personal projects (like text, images, or IoT devices) while keeping all private data safe on their own machines.
Small Business Insights
Small businesses can train smarter demand forecasting or recommendation systems by joining forces, while their customer data remains fully private and never leaves their store systems.
Freelancer Data Collaboration
Freelancers working on AI projects can train better models together without ever sharing their client data. Each freelancer keeps their work private while gaining the benefits of collective learning.
Open Data + Private Data Fusion
AI enthusiasts or small teams can combine open datasets with their own private data securely, improving accuracy without ever exposing what they want to keep hidden.
Community Energy Projects
Local energy groups or smart-home enthusiasts can collaborate to optimize power usage patterns across homes, while each household keeps its detailed data private.
Open Source AI Teams
Small distributed teams working on open-source AI can train models together across devices, without centralizing data, making projects more inclusive and secure.
Startup Collaboration on Limited Data
Small startups can pool model learnings together without ever sharing raw data. Each team keeps its data private but still benefits from stronger AI models trained on collective knowledge.
Researcher Collaboration
Independent researchers can work together by training models across their datasets without moving sensitive information. This enables collaborative projects that normally require big data access.
AI Enthusiast Communities
Hobbyists and AI learners can train shared models on personal projects (like text, images, or IoT devices) while keeping all private data safe on their own machines.