Artificial Intelligence

I like the dreams of the future better than the history of the past.

A perfect storm of change fuelled by digitization, mobilization and automation is turning science fiction to science fact, and forcing enterprises to digitally transform.

In today’s world, strategies can no longer be based on descriptive analytics, rather businesses need to push the limits of their traditional BI and analytics frameworks with predictive and prescriptive capabilities to generate faster and more accurate business outcomes.

From "what is happening?" or "what has happened?"
To "what will happen next?"

Welcome to the Edge

Build a Quicker, More Intelligent, Interconnected World

As the benefits of Edge Computing and Edge IoT materialize, so do the benefits of Edge AI (Artificial Intelligence), including:

  • Real-Time Processing: Edge AI enables immediate data processing, facilitating real-time decision-making and actions without the latency that comes with data transmission to and from the cloud.
  • Bandwidth Efficiency: By processing data locally, Edge AI reduces the volume of data that needs to be sent over the network, alleviating bandwidth constraints and potentially reducing associated costs.
  • Enhanced Privacy and Security: Local data processing means sensitive information doesn't have to leave the device, which reduces the attack surface for data breaches and ensures privacy.
  • Operational Reliability: Devices with Edge AI can operate independently of central servers, enhancing reliability, particularly in environments with unstable internet connections.
  • Scalability: Edge AI allows for the scaling of IoT solutions by adding more devices at the edge rather than overhauling central data centers or cloud infrastructure.
  • Energy Efficiency: Processing data locally can be more energy-efficient than sending it across a network to a data center, which is beneficial for battery-powered or remote IoT devices.
  • Cost-Effective: With reduced data transmission and cloud processing, there can be significant cost savings in cloud services and network infrastructure.
  • Customization: AI models can be tailored on a per-device basis, allowing for localized and context-aware intelligence that can adapt to the specific conditions of its environment.
  • Reduced Cloud Dependency: By enabling local decision-making, Edge AI reduces dependency on cloud services, which can improve operational resilience and reduce operational costs.
  • Compliance with Regulations: By processing data locally, Edge AI can help comply with data sovereignty and other regulatory requirements by not transmitting potentially sensitive data.

Harnessing the Power of Localized, Real-time Intelligence

AI systems are designed to simulate human intelligence by mimicking cognitive functions such as learning, problem-solving, perception, and understanding natural language.

By moving AI to the Edge, IoT is transformed from a simple data collection network into a distributed system of intelligent devices capable of sophisticated local data processing and autonomous operation, including:

  • Real-time facial and object recognition.
  • Enhanced predictive maintenance.
  • Real-time alerts for abnormal readings.
  • Localized behavior analysis.
  • Autonomous navigation, obstacle avoidance, and decision-making without latency.
  • Energy usage optimization.
  • Real-time tracking with predictive outcomes.
Predictive Maintenance in Manufacturing: Sensors in industrial equipment can use Edge AI to predict failures before they occur, reducing downtime and maintenance costs.
Smart Cities: Traffic control systems can process data from cameras and sensors in real-time to optimize traffic flow, reduce congestion, and enhance pedestrian safety.
Healthcare Monitoring: Wearable health devices can use Edge AI to monitor vital signs in real-time, providing alerts for abnormal readings and improving remote patient care.
Retail Experience Enhancement: In retail, Edge AI can analyze customer behavior in stores, optimizing inventory management and enhancing personalized shopping experiences.
Autonomous Vehicles: Self-driving cars rely on Edge AI for real-time processing of sensor data for navigation, obstacle avoidance, and decision-making without latency.
Smart Home Devices: IoT devices in homes, like thermostats and security cameras, use Edge AI to make immediate decisions about heating, cooling, or alerting homeowners to unusual activity.
Agricultural Optimization: In agriculture, Edge AI enables real-time monitoring and analysis of crop conditions, soil quality, and weather data, leading to more informed decisions on irrigation and harvesting.
Environmental Monitoring: Sensors can track environmental conditions like air or water quality in real-time, using Edge AI to alert authorities to pollution or hazardous conditions.
Supply Chain and Logistics: Real-time tracking of goods and vehicles, predictive analytics for delivery times, and automated warehousing operations are possible with Edge AI.
Safety and Security Systems: In security, Edge AI enables real-time facial recognition, anomaly detection, and threat analysis without needing to constantly stream video data to a central server.
Disaster Response and Monitoring: Edge AI can process data from sensors in disaster-prone areas, providing early warnings and enabling quicker response to events like earthquakes or floods.

The tip of the iceberg

Machine Learning and Artificial Intelligence technologies are only “the tip of the data processing iceberg”.

The deep substance is what is below the water line.

To be able to think about deploying Machine Learning and Artificial Intelligence enterprises must first establish “the deep substance” - the underlying databases and applications capable of delivering the data to Machine Learning and Artificial Intelligence processes together with the Cloud and Edge computing and storage architectures needed to support it.

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Underpin your AI initiatives with Syncrasy’s powerful, enterprise-ready, Acceleration Framework.

The tip of the iceberg

Machine Learning and Artificial Intelligence technologies are only “the tip of the data processing iceberg”.

The deep substance is what is below the water line.

Before Machine Learning and Artificial Intelligence technologies can usefully be deployed, enterprises need to access, curate and store all of their internal and external data sources, including previously disregarded data.

Unfortunately, for most of large enterprises meaningful raw data tends to reside in line of business systems across multiple business units, and often with overlapping and conflicting information. In addition, legacy databases, ETL and storage systems are not technologically consistent with Machine Learning and Artificial Intelligence technologies due to an explosion of data types and volumes, as well as the commercial requirement for actions based on real-time information.

To be able to think about deploying Machine Learning and Artificial Intelligence enterprises must first establish “the deep substance” - the underlying databases and applications capable of delivering the data to Machine Learning and Artificial Intelligence processes together with the computing and storage architectures to support it.

 

Syncrasy’s Core Technology

Fault-tolerant, distributed and extensible by design, Syncrasy’s Core efficiently manages elastically scalable COTs based computing resources, automates resource balancing to meet application requirements and supports full redundancy and high-availability of all deployed databases and applications.

    Syncrasy’s Software Defined Storage

    Syncrasy’s COTs based Software Defined Storage Solutions provide low cost, highly scalable options to store traditional and new data types and volumes with no vendor lock-in whilst at the same time allowing the re-utilization of existing legacy storage infrastructure.

    Syncrasy’s Run Anywhere Technology

    Fully optimized for bare metal, virtualized, cloud or hybrid infrastructures, Syncrasy’s Run Anywhere Technology lays the foundation needed to flexibly initiate and migrate these innovations with minimum investment and maximum scalability.

      Syncrasy’s Chameleon Technology

      Syncrasy’s Chameleon Technology enables the plug-ability of the modern, data centric applications needed to manage all data types and volumes, from connection through transformation and to delivery into data stores, data analytics and machine learning processes, data visualization tools and potentially triggering the automation of actions needed to optimize enterprise performance.

      Syncrasy’s Virtualization Technology

      A lightweight, low cost alternative to full machine virtualization, Syncrasy’s Virtualization Technology
      delivers high availability, live migration and automatic backup & restore for both modern cluster based applications as well as existing line of business applications.

        Syncrasy’s Pluggable Applications

        New data centric applications are appearing every day and no single technology is fit for every purpose, which is why Syncrasy incorporates pluggable choices from today’s best of breed applications across the whole data processing spectrum, and is committed to adding new technologies as they advance.

        Get your 1 hour free consultation today.

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        Syncrasy’s Transformational Technologies

        Syncrasy integrates pre-engineered hardware and software to provide the foundation and solution building applications needed to build early wins, explore opportunities and generate a “Flywheel Effect” that powers enterprise-wide digital transformation.

        Home
        Syncrasy Edge
        Syncrasy Cloud
        Use Case Spotlight
        Digital Transformation
        Docs
        Customer 360
        Big Data
        365 Enterprise
        Insurance
        Banking
        IoT
        Use Cases by Industry
        Resource Library
        Blogs
        Contact
        Contact
        Feedback

        Syncrasy’s Transformational Technologies

        Syncrasy integrates pre-engineered hardware and software to provide the foundation and solution building applications needed to build early wins, explore opportunities and generate a “Flywheel Effect” that powers enterprise-wide digital transformation.

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