Welcome to Indusify

Safe Collaborated AI for Industry


ABOUT

About Us

Indusify enables vendors of equipment like manufacturing machines, to continuously monitor their products operations throughout its entire lifetime and generate insights out of the collected data. Indusify enables the monetization of manufacturing machine’s operational data.
Indusify’s unique technology keeps the data private while enabling sharing it for advanced analytics technologies such as machine learning.



About Industry 4.0

The concept of industry 4.0 refers to the fourth industrial revolution.
This involves the digital transformation of the industry with the integration and digitalization of all the industrial processes that make up the value chain. Industry 4.0 represents a qualitative leap in the organization and control of the entire chain value throughout the lifecycle of the manufacture and delivery of the product.

The main benefits of Industry 4.0 are:
  • Greater productivity and better management of resources

  • Better and more efficient decision-making based on real information

  • Optimized and integrated productive processes

  • Reduction of manufacturing time both in the design and execution

  • Cost saving by better resource usage (workers, machines, material) and more efficient manufacturing process

Industry 4.0 paradigm shift is based mainly on the following principles:
  • Interoperability - the communication ability of all the elements of the factory, cyber-physical systems, robots, corporate information systems, smart products and the people, as well as third-part systems.

  • Real-time analytics - the ability to collect, process and analyze large amounts of data (Big Data) that allows the monitoring, control and optimization of processes, facilitating better operational and business decision making.

  • Service orientation - the ability to transfer the new value generated to the customer in the form of new services or improved services with the exploitation of new disruptive business models.









Management team

Indusify team brings dozens of years of professional experience in the software industry, networking, cloud and virtualization, machine learning and AI together with management skills and entrepreneurship experience.

Alon Harel, Co-founder, CEO Eyal Eshed, Co-founder, Chairman
https://www.linkedin.com/in/alon-harel https://www.linkedin.com/in/eshed/
Alon is a technology expert with an extensive experience in technological leadership with over 25 years in Embedded software, Networking, Cloud, Virtualization and IoT.
Alon has strong software development background together with deep understanding of software development process and life cycle.
Alon had served in various roles in R&D including software devlopment, product definition and system architecture.
Alon was part of several successful acquisitions in the industry, Chromatis/Lucent, Radlan/Marvell, Voltaire/Mellanox and others.
Alon holds a BSc. in Industrial Engineering summa cum laude, from BGU and an MBA from Colman.
Eyal is a serial entrepreneur with extensive experience in leading Hi-Tech companies from the initial concept to a successful company.
Currently Eyal serves as a board member and investor at several Hi-Tech companies and as the CEO of SpeakingPal which he co-founded.
Eyal was Co-Founder and Co-CEO of Omnivee, which he led from concept to a successful acquisition. Earlier in his career Eyal was a project manager and a software developer at Chromatis.
Eyal’s strong technology background is coupled with astute managerial capabilities.
Eyal holds a BA in Computer Science summa cum laude, from IDC, and an MBA from the Kellogg Graduate School of Management, Northwestern University.



























PRODUCT
  • Problem statement

    The Industrial Internet of Things revolution brings a promise of using big amount of data for a better decision making and data analytics processing like AI, in order to achieve higher productivity and better business performance. While using BigData for AI has already proved to have benefits in other market segments, its potential is still somewhat ‘artificially limited’ in segments like the manufacturing industry. The reluctance of firms to share their operational data, de facto, holds back the unleashing of the insights concealed in the aggregated data and prevents new business opportunities and great potential.



    Indusify platform

    Indusify’s platform applies data mining techniques to extract the value concealed in industry BigData while keeping data privacy, using a unique technology for ensuring a safe collaborated AI on the cloud.

    Bosch Corporate Research:
    “Indusify’s method has a computational cost that is orders of magnitude lower compared the FHE or SMC. For certain choices, Indusify’s method is definitely a viable and interesting solution.”



    Indusify solution

    Indusify’s platform collects data on customers facility or from customer’s cloud (in case it exists), masks it, so customer data is kept anonymous and private, and safely sends the data to a centralized location for processing while keeping customer’s data masked during the entire data processing phase. At this centralized location, an AI training process uses the data from all sources while it is kept in a protected masked format, to build a global model. This global model is much more accurate allowing all parties to benefit from the experience of each other, resulting in better insights and greater ROI.
    The platform does not intend to replace any encryption of the data while in transit or any credential-based access control to access cloud storage that store the data. Rather, the platform adds an ultimate protection layer by keeping the data in a masked form at all times, making it void of any context, making it impossible to link to any specific organization, facility or industry.
    The solution is comprised of three main building blocks:

    1. Data collection - either machines with built in capabilities to provide operational data or machines equipped with external sensors

    2. Masking module which can run on-premise (e.g. on Amazon GreenGrass core, Azure IoT edge runtime), or alternatively, can run on the machine operator cloud

    3. Global AI model constructed out of the data flowing from all connected machines and sensors and from all different machine operators




    The technology

    Indusify’s patent-pending masking technology, allows sending organization’s data in an abstract form, stripped from its private data and context, thus making it robust to eavesdropping while in transit and also tolerant to an event in which data gets to the ‘wrong’ hands while stored in the cloud.
    Our technology applies special masking algorithms to create a unique abstract form of the data that:

    • Keeps the exact inference model accuracy
      KPIs of Confusion matrix, Accuracy, Percision & Recall and F1 score for a clear text data model and a masked data model are 99.99% equal

    • Does not require additional compute resources
      Comprehensive enough to be processed by cloud-based Machine Learning and other data analytics tools services without paying the compute power penalty involved in processing encrypted data.
      Additional computation time involved for processing a dataset of ~1GB is negligible (< 10 seconds).

    Value

    Indusify platform unleashes the power of data collaboration.
    Machinery vendors, which could not get the valuable data their machines produce, can now receive, process and leverage this data while ensuring the privacy of their customers data.

    Indusify platform brings significant value for both machine vendors and their customers, resulting in a win-win situation.

    Machine vendors benefit from:

    • Life-long quality assurance
      The ability to find machine malfunctions in scenarios which could not be tested during in-house QA process

    • Early fault detection Fast reaction, preparing support/maintenance team/getting substantial parts for fixing

    • New service offering
      Tailored usage recommendations based on customer behavior and use, KPIs (e.g. utilization, breaks, cycle-time), predictive maintenance

    • Better customer relationship
      Prepare ahead to handle upcoming problems, better customer needs addressing

    • Continuous product improvement
      Improve the product along its entire life

    • Better future products
      Better design and fit for real market needs and use cases

    Machine users enjoy the benefits of BigData data analytics from the very first moment

    • Predictive maintenance
      Benefit from a mature, comprehensive, and reliable predictive maintenance service which its model is being continuously trained based on the maximum amount of data which represents varied use cases and operational conditions from a large customer base.

    • Access to valuable insights
      Without the hassle and the knowledge required for maintaining and operating data analytics platform in-house

    • Better service from machine vendor
      Improve the product along its entire life

    • Better future products
      Faster response and problem fixing











































































SERVICES
  • Anomaly detection

    Anomaly detection (aka Outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
    Typically, anomalous data can be related to some kind of problem or rare event such as bank fraud, structural defects, malfunctioning equipment etc. These relations (with significant events) makes it very important to be able to pick out which data points can be considered as anomalies, as identifying these events are typically very interesting from a business perspective. That brings us to one of the key objectives: How do we identify whether a data point is normal or anomalous? In some simple cases, as in the example below, data visualization can give us important information. Anomaly detection (aka Outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

    However, the data is usually much more complex, while anomalies could be a combination of multiple variables over multiple time series. There are groups of mathematical methods that reveal anomalies, like Dimensionality reduction using Principal Component Analysis (PCA), Artificial Neural Network and others. Normally, a combination of multiple methods is tailored for any specific case.
    Indusify specializes in analyzing manufacturing data, defining the relevant datasets and applying the best algorithmic solution for detecting anomalies that have operational and business value.
    Indusify provides the service as a turnkey solution, starting from research all the way to a working anomaly detection software solution.


    IIOT software services

    In addition to the platform and the solution that Indusify provides in the field of safe data collaborative analysis, the company also provides complementary professional software services in these fields. The software services may be combined with the company’s products or as a pure service to support and extend customer’s own IIOT platform.






CONTACT US

Contact us:

phone: +972-54-2490512

icon 1Shoham, Israel

icon 3www.indusify.com

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