As IoT continues to dominate industrial applications and billions of machines, sensors and applications are generating data at the edge, organizations are increasingly needing to operate beyond the cloud.

Leveraging AI to automate mission-critical services across a edge-to-cloud deployment model will fundamentally change the way organizations gather and respond to this data to make better operating decisions in real-time.

The Right Solution for Edge-to-Cloud Deployments

The AdapdixTM software platform is a fully integrated edge-optimized AI/ML platform suite offering different modules that allow flexible AI/ML deployment across the enterprise.

The individual software modules are designed to act separately and independently in distributed, yet, fully synchronized fashion to allow for rapid development, innovation and management across the enterprise.

Product Features


(Reliability and Integration)

EdgeSite is the product that fuels the engine of the EdgeOps platform. The EdgeSite software is distributed on site, on the factory floor, machine, endpoint or even in the cloud.

  • The software functions as part of a containerized workload to translate and ingest disparate data from highly distributed endpoints in deterministic pattern
  • The endpoints are either connected to an enterprise mesh architecture or run remotely with a local EdgeMesh


(Service Performance)

EdgeMesh is the work engine of the EdgeOps suite. It provides data engineers and data scientists a coordinating environment to configure, manage and maintain highly distributed, edge-optimized AI/ML-based data.

  • EdgeMesh’s patented approach provides full stack visibility from data ingestion to AI/ML application observability in a time-synchronized manner
  • Customers can view data streams for the purposes of detecting anomalies for predicting system performance in order to improve operational efficiency
  • EdgeMesh synchronizes data between EdgeSites, production line, as well as collecting data from other sources such as test data, operator data and off machine computer systems
  • Real-time data processing, performance monitoring and tracing, and quality of service are among the many features of the service mesh


(Development and Scale)

EdgeFlexx is the primary entry point for many customers. EdgeFlexx is data-centric, edge-optimized software for AI/ML data efficiency enriched for data integrity, connectivity and security.

  • Orchestrates and synchronizes AI/ML data exchanges between edge-to-cloud data exchanges in a deterministic, ultra-low latency fashion
  • Provides for ML Ops, AI/ML training and global application orchestration with seamless integration and observability of all data streams
  • Handles software updates, API integration, third-party applications, enterprise connections, and automatic deployment allowing platform scalability

AdapdixTM EdgeOps Stack

A Simple Approach to

Cloud-to-Edge Deployments
  • Adapdix’s EdgeOps products aim to develop applications and solutions that will lead to innovative solutions in data capacity, latency, data efficiency, data scalability, and energy efficiency.
  • A new connectivity and computing full stack architecture provide a new approach to innovation in industrial environments. EdgeOps architecture centers on industrial automation at the edge.
  • Our platform is optimized for ultra-low latency environments where real-time data management, seamless integration of AI/ML analytics and adaptive control is required.

Benefits Across the Organization

Benefits Across the Organization:
  • Accelerate your journey to an AI-Based operating model
  • Drastically improve performance across the supply chain
  • Reduce complexity and unplanned downtime of services
  • Edge-optimized uptime management increases production uptime and reduce operational risks
  • Optimize operations, redefine employee experiences, improve customer satisfaction, create differentiated business models
  • Less space, less energy, lower expenses, higher performance
  • Increase Revenues. High equipment efficiency delivers high quality output and thus a continuous, uninterrupted stream of revenues for the manufacturer
  • Drastically reduce downtime. Time is money, and downtime rapidly becomes very costly – particularly unscheduled downtime
  • Reduce Total Cost of Ownership. An AI-based digital operating model will drive significant OEE improvements and lower cost over the entire machine lifetime
Technical Teams:
  • Use AI to control, categorize and deploy the necessary infrastructure, applications, services and connectivity across a cloud-to-edge deployment model. AdapdixTM provides a purpose built service mesh for performance, QoS, and reliability.
  • Automate and scale distributed services to maintain high performance digital environments and avoid human intervention
  • Provide a real-time directory of all running services to improve application inventory management
  • Enable visibility into services and their health status to enhance health and performance monitoring

Customer Use Case

AdapdixTM customers, with the use of EdgeOps, can achieve significate improvements in their enterprise operations. A typical large customer deployment of EdgeOps in 2018 has yielded:

Ongoing Innovation
& Development

  • The CI/CD pipeline is one of the best practices for DevOps teams to implement, for delivering code changes more frequently and reliably. AdapdixTM supports CI/CD pipeline process for businesses that want to automate software deployments to their site. Controlled distribution of new software releases for frequent improvement of service applications ensures a safe and reliable delivery process.
  • AdapdixTM supports CI/CD pipeline process for businesses that want to automate software deployments to their site. Controlled distribution of new software releases for frequent improvement of service applications ensures a safe and reliable delivery process.

A Platform for
Data-Driven Solutions

Customers gravitating towards new operating models


Enabling Industry Solutions:

  • Predictive Asset Optimization & Uptime Assurance
  • Adaptive Control
  • Predictive Operational Efficiency and Remote Observability
  • Machine Learning Orchestration
  • AI-based Product Quality and Compliance Control
  • Quality, Testing and Safety Compliance