Blockalytics - Platform

Intelligent Insights from Distributed Data with Gen AI

Blockalytics leverages Generative AI to unlock insights from distributed data silos across locations without moving data to the cloud. By applying AI directly to data sources on-site, Blockalytics provides a cohesive view of your organization’s data landscape while maintaining privacy and compliance. This approach allows you to analyze complex, disparate data sources to extract intelligent insights, no matter where the data resides.

Cost Savings by Eliminating Data Logistics: Avoid the high costs and complexities of moving data to the cloud, as Blockalytics analyzes data where it’s stored, reducing the need for cloud infrastructure.
Privacy and Compliance Built-In: Maintain data privacy by keeping data on-site, ensuring full compliance with data residency and regulatory requirements.
Real-Time, Low-Latency Insights: Generate insights in real-time without cloud latency, allowing for faster and more responsive decision-making across your organization.

Pre-Built Tools

Blockalytics’ Scarlet™ SDK empowers data scientists to effortlessly build, deploy, and manage advanced AI and ML models directly on Edge devices. Equipped with a library of optimized, pre-built algorithms—including industry-specific models for manufacturing—the SDK enables users to start with ready-made solutions or create tailored models suited to their unique needs. This approach allows for secure, collaborative analytics across geographically distributed sites without needing to move data.

Full Customization: Develop and deploy models tailored to your specific business challenges.
Time-Saving Pre-Built Models: Leverage optimized algorithms for common applications right out of the box.
Seamless Distributed Analytics: Enable secure collaboration and analytics across multiple sites without data transfers.

Collaborative Learning

With Blockalytics, your Edge devices work together to train and improve a shared model, all while keeping data safely on-site. Collaborative (or federated) learning means your models get better insights without moving sensitive data, making it perfect for privacy-focused industries.

Get better model accuracy from all data sources without sharing data.
Ensure privacy by keeping data where it is.
Ideal for large, distributed networks with diverse data sources.

Flexible Architecture

Blockalytics offers flexible deployment modes to meet your operational needs. Choose between connected Edge, hybrid Edge-cloud, or multi-cloud integration, adapting the platform to fit your strategy. This versatility provides the best of both worlds—Edge processing for fast, on-site analytics and cloud connectivity when needed.

Scale seamlessly across Edge and Cloud environments as your needs evolve.
Customize deployment to align with your infrastructure and business goals.
Optimize for both on-site processing and cloud-based flexibility.

Decentralized Orchestration

Blockalytics takes the hassle out of managing analytics across multiple devices. With decentralized model orchestration, you can group devices for targeted deployments, easily roll out updates in stages, and monitor performance across all Edge devices from a single platform. Real-time updates keep your models in sync and performing their best, with the flexibility to manage groups individually or as a whole.

Targeted Deployments: Deploy specific models or updates to relevant device groups, ensuring each device gets only the analytics it needs.
Streamlined, Staged Updates: Test updates in one group before deploying across the network, ensuring stability and accuracy.
Simplified Model Monitoring: Manage large-scale device groups with ease, without relying on a central server.

Model Monitoring

Track, manage, and optimize model performance with ease. Blockalytics' version control and model registry let you roll back to previous versions, test new updates, and keep your models organized across the network. The platform also continuously monitors model accuracy, detects signs of data and context drift, and recommends retraining to ensure models stay accurate and effective.