Blockalytics provides federated industrial analytics for IoT Edge applications, enabling real-time insights from distributed data without transferring it to the cloud. This approach reduces costs and latency while ensuring data privacy and compliance, making it ideal for industries requiring secure, on-site analytics.
Funding
$255.8K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many industrial IoT deployments generate vast amounts of data at the edge, but transferring this data to the cloud for centralized analytics can be expensive, introduce latency, and raise privacy concerns. Traditional cloud-based analytics solutions may not be suitable for applications requiring real-time insights or those subject to strict data residency regulations.
Solution
Blockalytics provides a federated analytics platform that enables real-time insights from distributed IoT edge data without requiring data to be transferred to the cloud. The platform leverages collaborative AI models and pre-built tools to extract insights from data silos on-site, preserving data privacy and compliance. Its flexible architecture supports connected edge, hybrid edge-cloud, and multi-cloud deployments, allowing organizations to perform fast on-site analytics with cloud connectivity as needed. Blockalytics streamlines multi-device analytics with features like grouped deployments, staged updates, and real-time model synchronization, all managed from a single platform.
Target Audience
The primary target audience includes industrial enterprises in manufacturing, energy, and supply chain management that require real-time analytics on edge data while maintaining data privacy and minimizing cloud dependency.
Features
- Federated analytics for processing data at the edge, reducing latency and bandwidth costs
- Collaborative AI model training across edge devices without moving sensitive data
- Scarlet™ SDK for deploying AI/ML models on edge devices with secure, distributed analytics
- Pre-built and custom algorithms for various industrial applications
- Flexible deployment options: connected edge, hybrid edge-cloud, and multi-cloud
- Decentralized orchestration for managing multi-device analytics with grouped deployments and staged updates
- Model monitoring and maintenance with version control, model registry, and drift detection
- Gen AI integration for extracting insights from distributed data sources