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Revolutionising education: How AI in Edtech is transforming learning worldwide

Adapting AI and ML to Edtech has some potential benefits that outweigh its threats. Here’s what experts have to say.

By India Today Education Desk: Artificial intelligence (AI) has revolutionised many industries. The education industry is no different and gaining positive momentum with the adoption of AI. To achieve diverse and improved outcomes, AI can be applied to school-based learning, professional learning & assessment, lifelong learning, and enterprise workforce learning & assessment.

The technology and its associated technology, including machine learning (ML), deep learning, natural language processing and computer vision are restructuring the way educators approach learning & assessment and gaining the confidence of researchers and businesses alike.

Senthil Devarajan, Global Industry Head – Media, Entertainment & Education at Ness Digital Engineering has shared some of the potential benefits that outweigh the threats of adapting AI & ML to Edtech.

Read the full article here.

Beyond the Nastygram: Cloud FinOps for the Enterprise with Ness Digital Engineering

By Subir Grewal, Global Head, AWS Practice – Ness Digital Engineering
By Maysara Hamdan, Partner Solutions Architect – AWS

Imagine the following scenario: a business unit could save the enterprise $15 million a year in proprietary database licensing costs by migrating to open-source databases. The cost of migration is estimated at under $10 million, providing positive return on investment (ROI) within eight months.

Nevertheless, the business unit will not fund this migration. Why not? The licenses are paid for from an enterprise-wide pool, whereas the cost of migration would have to be funded by the business unit out of its own IT budget. This is a prime example of organizational misalignment.

Read the full article here.

The Adoption of Digital Twin Technology in Manufacturing & Transportation

Internet, smartphone, and tablet are among the three fastest-adopted technologies since technology adoption is measured.

Today’s pace of technology adoption shapes businesses across all industries.

While this disruption might get perceived as a threat by many, it is primarily a significant opportunity for those capable of harnessing it.

Physical and digital worlds are blending more than ever before.

The mass adoption of digital twin tech enabled by industrial IoT may result in one of the most significant productivity boosts for manufacturing and transportation since the invention of the assembly line.

History remembers 2002 as the year when digital twins got attention in the commercial sphere.

A consultancy firm Challenge Advisory was invited to host a lecture at the University of Michigan, where it proposed using computer modeling and simulation to create a virtual representation of a product lifecycle management center.

Over the following decades, digital twin adoption have expanded to various industries and applications. As a result, technology has become essential for optimizing operations, improving quality, and driving innovation.

However, widespread digital twin adoption accelerated only in recent years with the advances in IoT, AI, and cloud computing, enabling organizations of any size across industries to create and manage digital twins at scale.

What is a Digital Twin? Digital Twins Explained

The digital twin is a virtual representation of a physical item or process.

Digital twin is visualized as a 3D model with a comprehensive set of attributes updated in real-time with data from sensors located within the object and its environment.

The visualization helps to accurately represent its real-world counterpart’s structure, behavior, and performance.

Digital twins can be used for real-time monitoring and simulations, providing valuable insights, and enabling users to make informed decisions about the design and operation of complex systems. Here is a digital twin data model.

Figure SEQ Figure \* ARABIC 1: Digital Twin Model Schema

Digital Twin Uses

• Apollo 13 – First Known Use of Digital Twin

The first known use of a “digital twin” can be traced back to the early days of modern space exploration.

In 1970 a digital twin played a vital role in the successful outcome of the Apollo 13 mission.

During the mission, an oxygen tank exploded on the spacecraft, causing a loss of power and life support systems.

The digital twin of the spacecraft, created before the mission, allowed engineers on the ground to diagnose the problem, simulate different resolution scenarios, and develop a plan to bring the astronauts home safely.

Since then, digital twins have become essential to the most complex engineering endeavors.

• Digital Twins in Manufacturing

In manufacturing, digital twins can be used to simulate and analyze the characteristics of a product or system before deployment.

Businesses can now validate their ideas in advance, in a simulated environment, without physical prototyping.

This significantly increases the product iteration speed while at the same time decreasing its development costs.

After the deployment, a digital twin can keep track of the product’s performance over time which allows the identification of potential problems and inefficiencies before they occur.
For example, a machine’s digital twin could simulate its operation and predict when it will likely require maintenance based on factors such as temperature, vibration, and wear and tear.

This information can be used to schedule a repair at the most convenient time, avoiding unexpected downtime and ensuring that the machine continues to operate at peak performance. GE is one of the companies using digital twins.

An excellent example of this is GE Power which leverages digital twins to optimize the maintenance of its wind turbines. Using digital twins to predict when care is needed has improved their reliability and reduced maintenance costs.

Figure SEQ Figure \* ARABIC 2: Digital twin of a GE Power Wind Turbine

• Digital Twins in optimizing fleet performance

Digital twins are becoming increasingly common in the transportation industry.

They are used to analyze the performance of vehicles, trains, and ships and optimize the routing and scheduling.

With predictive maintenance, digital twins in logistics can predict potential maintenance issues to avoid costly downtime and enable just-in-time maintenance to reduce the number of visits to the maintenance shop.

In fleet management, digital twins provide operational and environmental context by capturing data about a vehicle’s location, speed, direction, and surrounding environment, such as traffic conditions and weather.

This allows fleet managers to gain a comprehensive view of their fleet and to identify and address potential problems and inefficiencies in real time.

Accelerating Digital Twins Implementation with Cloud

Cloud digital twin technologies, such as AWS IoT TwinMaker and Azure Digital Twins, can significantly speed up the adoption of digital twins.

They provide a powerful toolset for creating and managing digital twins, including visual modeling and simulation capabilities, real-time data integration and analytics, and seamless integration with other systems or services.

By using cloud digital twin technologies, organizations can significantly reduce the time required to create digital twins and begin leveraging the benefits of the technology without the need for extensive in-house expertise or infrastructure.

Harnessing the full potential of these tools can help organizations speed up their digital twin adoption and gain a competitive advantage.

Figure SEQ Figure *\ ARABIC 3: Windfarm representation in Azure Digital Twins

Standardizing Digital Twins

Standardizing digital twins defines a common set of rules and approaches enabling different digital twins to be integrated and shared, allowing for better collaboration and interoperability among other systems and organizations.

The Digital Twin Consortium is a group of organizations to develop and promote standards and best implementation practices for digital twins.

The consortium provides a forum for member organizations to share their knowledge and experience with digital twins and to collaborate on developing standards and guidelines for their use.

By promoting the standardization of digital twins, the Digital Twin Consortium helps to accelerate the adoption and integration of this technology in a wide range of industries and applications.

Figure SEQ Figure \* ARABIC 4: “Digital Twin Capabilities Periodic Table” as defined by Digital Twin Consortium, provides
a foundation of requirements to be assessed before designing Digital Twin implementations

Maximize the potential of digital twins with Ness Manufacturing & Transportation Center of Excellence

As an AWS Premier Partner and Microsoft Azure Gold Partner, Ness is one of the few digital twin companies having the expertise and experience to help your business understand the digital twin benefits and identify the areas where they can provide the most value.

Our proven track record of success makes us the perfect partner to develop proof-of-concept projects that demonstrate these benefits in a real-world setting.

Don’t miss the opportunity to revolutionize your business with digital twins – partner with us today!


What are the types of digital twins in manufacturing?

Digital twins are extensively used in manufacturing, some of them include Product Digital Twin, Process Digital Twin, Factory Digital Twin, Asset Digital Twin, and Performance Digital Twin.

What are the benefits of digital twins for manufacturing?

Benefits include better product design, improved efficiency in production, reduced downtime, optimum resource utilization and superior production quality.

What is digital twin model?

A digital twin model is virtual replica of a physical machine, equipment, or a process. it is developed using real time data, mathematical models, and simulation. The model can replicate the same behavior, characteristics, and attributes of the real-world physical object.

What are the 4 pillars of digital twin?

The first pillar on the physical machine, equipment or process which is represented by the digital twin. The second pillar is the digital twin itself which is a digital replica of the physical entity. The third pillar is data integration, here real time data form the physical entity is collected, processed, and integrated into the digital twin model. The fourth pillar is AI and Analytics, which is used to derive insights, predict behaviors, and optimize performance.

Which industry uses digital twins?

Digital twins are most commonly used in manufacturing, energy and utilities, aerospace, defense, healthcare, automotive industries.

A Global Leader in personal systems, printers, and 3D printing solutions achieves a Seamless Financial Data flow through a Simplified Company Finance Solution with improved forecasting

Case Study

A Global Leader in personal systems, printers, and 3D printing solutions achieves a Seamless Financial Data flow through a Simplified Company Finance Solution with improved forecasting​

The solution helps customers with a near real-time holistic view of the company’s financial and contractual information, including data analysis for AI/ML and reporting.


The client is a global pioneer in computing and printing innovations with excellence and customer-centric products.


Appropriate (expected) corrections to revenue based on rebate and promotion programs were determined using data from multiple sources. Ness delivered data transformation and automated the process while adding the needed governance. Ness built Data engineering pipelines to support AI/ML and reporting solution and developed writeback, workflow tool using Power On /Power BI, and role-based security implementation.


Ness delivered a comprehensive ETL and Business Intelligence solution to transform their data ingestion structure and provide actionable insights into the Contractual Finance function. Using ADLS, Azure Databricks, and Azure Data Factory, Ness implemented a purpose-built solution to ingest data from multiple sources and create an Integrated Data Hub. This data hub made an abstraction for performing the analytics on only the views for downstream ML Analytics and Reporting purposes, thus achieving high performance and delivering near real-time insights.


The end customers could make important financial decisions using the insights to adjust the financial strategy and resource allocations accordingly. This delivery did not disrupt daily operations. The codebase was compliant with strict acceptance standards and passed stringent UAT tests.

A media SaaS firm digitally transforms its platform and achieves exponential growth

Case Study

A media SaaS firm digitally transforms its platform and achieves exponential growth

Ness enabled the client to transition into an Intelligent Engineering Center (IEC) to establish dedicated models at global locations.


Our client is a leading provider of trusted media and influencer outreach solutions that helps build relationships while securing expanded quality coverage.


Poor People/Talent Management: 
  • Managing multiple vendors scattered across different locations.
  • High attrition due to part-time employment
  • Talent acquisition and management 
Loss of knowledge 
  • Lack of Document management
  • Establishment of service-level agreements
Immature engineering practices leading to inefficiencies in scaling teams 
  • Consolidate technology for efficiency and compatibility.
  • Automated testing for faster development and quality control.


Ness worked with the client to transition into an Intelligent Engineering Center (IEC) that is co-managed by both parties. The Intelligent Engineering Center is Ness’s proprietary model that helps clients establish dedicated models at any global location. It offers benefits such as access to global experts, setting up extended teams with flexible engagement models, and leveraging Ness’s proprietary engineering competence.

As a first step, Ness conducted due diligence to establish improvement levers across the Financial and Engineering domains. The next step was to establish a dedicated IEC.

Solution highlights 
  • Extend teams to ensure cultural continuity and establish a knowledge hub within the company.
  • Employ an agile methodology to enhance engineering maturity, scalability, and cost-effectiveness.
  • Attain engineering excellence through agile assessments and utilized data-driven metrics.
  • Establish a knowledge transition process to improve risk management and continuity.


From outsourcing arrangements to value-driven partnerships, Ness demonstrated the breadth of experiences, resources, talent, and knowledge helping the client to transition to a value-driven journey leading to:

  • Redesigned units into cross-functional teams for engineering maturity and cost reduction delivering Up to 40% reduction in outsourcing spend
  • Implemented an SLA-based model to ensure it meets the goals of predefined metrics, delivering 20% improvement in SLA

MQTT in IoT world

What is it good for?

MQTT is an OASIS standard messaging protocol for the Internet of Things (IoT). It is designed as an extremely lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small code footprint and minimal network bandwidth.

From the sea of protocols aimed to send data from clients to the cloud, MQTT is the most suitable for IoT use cases. Nowadays, we see it more and more used in real projects. Its implementations are confidently holding the position of reliable communication platforms for environments with unreliable connection and thousands of small devices.

As this lightweight protocol is best suited for IoT solution, we will look on different aspects and products for its usage in our projects.

Nowadays in IoT systems like factories, production plants, homes and digital twins, the most used protocols to transfer data from sensors to cloud are AMQP, MQTT and CoAP. Every option has its advantages and disadvantages in different scenarios so why is then MQTT used the most? To answer this question, we have to compare them and evaluate their usage.

But first, let’s talk about one of the key features of any type of communication middleware, Quality of Service. To have better picture of reliability of services, we distinguish three types:

QoS 0 - fire and forget

  • At most one sent message is received. If there is any failure in network, communication or supporting applications, message is not delivered.

QoS 1 - at least once

  • Every message is delivered to the receiver. Due to failures, multiple same messages or messages out of order can be delivered.

QoS 2 - exactly once

  • Messages are delivered exactly once in same order they were sent.

Advanced Message Queuing Protocol is an open standard message-oriented protocol which features are orientation, queuing, routing, reliability and security. Previous API level middleware focused on standardizing programmer interaction with different implementations, rather than on providing interoperability between multiple implementations. It supports wide variety of messaging applications and communications patterns including request/response and publish/subscribe and Quality of Service 0,1 and 2 using TCP protocol. It has general capability to transfer up to 1000 messages per broker instance.

Constrained Application Protocol is optimized for constrained low-power devices in environment with unreliable network. CoAP is designed to easily translate to HTTP for simplified integration with the web, while also meeting specialized requirements such as multicast support, very low overhead, and simplicity. It supports request/response communication with Quality of Service defined as confirmable and unconfirmable messages. Using UDP, it supports transfer of up to 100 messages.

Formerly Message Queuing Telemetry Transport but now, MQTT abbreviation means nothing specific. It is designed as an extremely lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small code footprint and minimal network bandwidth. Supporting QoS 0,1 and 2 can using TCP transfer up to 1000 messages per one broker instance.

When our goal is to transfer

  • the most messages,
  • from constrained devices,
  • easiest way,
  • with option to use QoS 0 to 2,
the best choice is MQTT.

But how it works?

Whole publish/subscribe communication has three types of actors.

  • Publishers publishing messages to the broker to specific topic and queuing them in case of network unavailability.
    • Advanced sensors, edge devices and PLCs producing telemetry and sensory data
  • Subscribers subscribed to specific topic or group of topics receiving messages.
    • Backend services, message busses, other IoT devices, analytical applications, time-series databases and client devices for further processing
  • Broker receiving messages and making sure all subscribed clients will receive the message.

In 2019, new version of MQTT came out with some very interesting additions that keeps protocol as lightweight as before but provides more options to manage connections and notify clients about different events they can be interested in. New version is naturally compatible with old version so old MQTT v3.1.1 messages are not a problem when communicating with a broker.

  • Challenge/response authentication in addition to username/password
  • “last will” message. When connecting to the broker, client can provide message, that broker will send to all subscribers in case of “not clean” disconnection
  • Interval in CONNECT packet allows broker to drop the client if time after disconnecting elapsed
  • Client can limit number of QoS1 and QoS2 messages it is willing to process (note: flooding a client with QoS0 still possible)
  • Broker can send the client a disconnect message and reason code e.g. 137 Server busy
  • Message expiration to prevent clients from receiving stale data (retain forever in v3.1.1 for clean session flag and QoS 1 or 2)
  • Client-to-broker and broker-to-client topic aliases to further reduce packet size
  • Shared subscriptions let the broker distribute incoming messages to a group of “competing consumers”, only delivering each message to one of the groups (in non-shared, message is delivered to all subscribers)
  • Subscription flags Retain as Published, No Local, and Retain Handling
  • Subscriber IDs: optional integer identifier to route message to handler (instead of parsing the topic)

How individual brokers can be different?

Brokers can differ in their internal implementation and provide different set of features that can be crucial in specific use cases, so it is not trivial task to choose one over another. The most clear view on the topic is division into next few categories:

TCP Retransmission broker
This is the simplest broker we can use. It is single, non-clustered broker where we rely only on TCP retransmission for ensuring the delivery of message. Every other failure in the chain of transfer process will result into not delivered message.

Publication Acknowledgements broker
Broker is using acknowledgement notices of received messages by broker and also subscribed clients so message can be resent in case of a failure.

Single instance Broker with Persistence
For the case of broker failure and need to restart broker instance, this type of brokers uses persistence volume to store client’s session data with message queues. With option to use distributed storage, it can be immune also against disk failure.

Clustered Broker
When using clustered broker, even one broker instance failure will not affect broker availability because there are other instances already running. Only already sent messages will be lost in case persistent volume not configured.

  • VerneMQ
  • EMQ-X3

Clustered Broker with Queue Mirroring
With mirroring of user session and received messages to other nodes, this is the most reliable broker type. As nothing has only advantages, mirroring comes with a slight delay in message processing, so this feature is useful in specific cases. 

  • ​​​​​​​Only HiveMQ

But we can still use proprietary implementations

AWS IoT Core

Azure IoT Hub

Yes, when working with cloud solutions, incorporating MQTT capability for communication with devices is trivial task. Just enable AWS IoT Core or Azure IoT Hub, use Azure or AWS MQTT client on its edge device runtime and you are good to go. Great advantage is fast learning curve and ease of use in standard use cases and general implementations. Everything is working as intended but problems can arise after a time of usage, when system will become robust, or when customer choose to change cloud provider.

Brokers are a bit of a black box with not much options to configure or trace and debug problems and MQTT protocol is not fully supported. Proprietary implementation also lacks MQTT v5 support and QoS 2, that can be problem in future. As MQTT protocol implementation is custom with slight differences, it is hard to switch edge device runtime, device onboarding runtime or cloud platform.

Dependency on platform can be big problem when cloud provider decides to change pricing model of its services. Customer business model can be dependent on service cost per device/data/deployment and change can make it not feasible.

How MQTT can help in transportation

Every part of our daily life is in some way affected by IoT. Transportation domain is not different and using IoT approach to problems, it can be even more driven by innovation and data. MQTT is therefore important tool that makes it easy to connect complex IoT infrastructure and enables us to focus on data processing and process optimization. Among the solved problems of transportation devices communication are:

  • Persistent Always-on Client Connection: pub/sub architecture allows vehicles to be decoupled. Allows persistent, always-on push connection to the cloud.
  • Guaranteed and Reliable Data Delivery: advanced message retention policies and offline message queuing are essential to accommodating network latency and unreliable mobile networks.
  • Secure non-addressable clients: Vehicles or devices running MQTT clients are not addressable over the Internet. This makes it virtually impossible for a vehicle to be directly attacked by a hacker from the Internet.
  • Efficient network utilization: MQTT was designed specifically to operate over constrained networks. HTTP deals with requests one at a time, with overhead such as authentication being carried out each time. In one HiveMQ test scenario, 100 messages sent using MQTT required 44,748 bytes, compared to 554,600 bytes for 100 messages sent using HTTP.
  • Elastic scalability and Auto Heal: user does not see any change in user experience when nodes are started or stopped since the connected vehicle, device or sensor can resume its session on any of the remaining cluster nodes.
  • IoT observability: HiveMQ tools allow for a system administrator to pinpoint issues with a specific vehicle and work to resolve the issues. This allows administrators to do real-time fleet monitoring and remote debug and trade of interactions between a vehicle and the broker.

Written by Gabriel Novak, Senior Software Engineer at Ness Digital Engineering

Where is the business logic in digital twin-based fleet management solutions?

A fleet management solution comprises two component groups. The first group focuses on asset management, driver behavior, usage reports, general reporting, analytics, and tracking of the current and historical positions and states of vehicles. These aspects provide valuable information to fleet management personnel, enabling them to consume the fleet’s data comprehensively.

The second component is operational, providing contextual information about the fleet’s ongoing task execution. It establishes connections between entities within the fleet domain, such as drivers, vehicles, current trip plans, destinations, weather conditions, and traffic situations. This contextual information is essential for fleet management to make informed decisions. When the context encompasses all the necessary entities and integrates with external tools, it facilitates informed decision-making and enables automated decision-making.

We will delve deeper into the operational aspect of a fleet management solution that can significantly benefit from an implementation based on the digital twin concept. We have developed an end-to-end solution as part of our exploratory efforts and approach validation. The solution demonstrates data flow and connections between various components.

Digital twin key features

The digital twin concept offers numerous utilization options, and many solutions take advantage of a relevant subset of its features. In our implementation, we have incorporated the following key features:

  • Situational awareness
  • System-of-systems
  • Simulation

Situational awareness relies on a digital twin model that accurately represents entities, properties, and relationships within the fleet domain. Twin modeling is elastic in respect of future updates. It is better to start with a small twin model that covers initial functionality and expand it in the future as needed. The twin model’s instance reflects real-world fleet objects and can continuously update with real-time data.

This comprehensive contextual information serves as the foundation for developing services on top of it. Situational awareness ensures reliable information for decision-making. It provides an equivalent experience to physically looking out of a window to count vehicles in a parking lot or obtaining such information from a dashboard. It is true even for complex details that may not be easily observable in the real world.

The digital twin also serves as an integrator, playing a crucial role in adopting a system-of-systems approach to gather data from various fleet management components and form the current state of the fleet. Once collected, data is ingested into digital twin properties or referenced to the information source. Each digital twin graph entity consolidates all the necessary information to support decision-making and downstream services.
We can leverage a digital twin model for simulations once we have a digital twin model. The first group of simulations involves testing business logic, decision logic, and automation implementation by simulating different fleet scenarios. It allows us to assess the effectiveness of our strategies in complex situations that are difficult to record and replicate in the real world.

The second group of simulations focuses on evaluating the feasibility of a fleet plan. These simulations consider predictions such as weather and traffic conditions and the probability of events such as vehicle failures. By considering these factors, the simulations assess the executability of the plan and calculate a risk score.

Business and decision logic component

Our solution’s digital twin-component represents the fleet’s current state but does not inherently contain business logic. Let’s consider a scenario where a vehicle is assigned to visit multiple destinations for customer maintenance work, with customers notified in advance about the scheduled visit time window.

The vehicle continuously sends its position and telemetry data, and a subset of this data goes through the IoT platform to the Digital Twin Graph. When the vehicle’s digital twin receives the position update, it triggers a monitoring service that calculates whether the current state aligns with the planned itinerary. All the necessary information for evaluation is either in the digital twin graph or pulled from specific services.

An alert is triggered if the estimated arrival time exceeds the scheduled time window. The decision logic can be complex, incorporating factors beyond comparing estimated arrival times. For instance, customer service levels may dictate different maximum waiting times based on customer status. A subject matter expert (SME) should manage such decision logic and may require periodic modifications to adapt to changing requirements.


To tackle decision complexity and provide a tool for subject matter experts (SMEs), we have utilized the DMN (Decision Model and Notation) modeling language. DMN offers a precise specification of business decisions and rules, making it understandable for various stakeholders involved in decision management. The graphical notation used in DMN is directly executable through an interpreter.

We have adopted a microservice approach in our implementation, leveraging the Kogito framework powered by Quarkus. It allows us to package decision diagrams into containers and execute them in a cluster as required. By utilizing this approach, we ensure scalability and flexibility in managing decision-related processing. DMN example:

DMN is stateless. One observation of a delay should not immediately trigger an alert. It is important to track the development of the delay and determine the appropriate course of action for a remedy process. To achieve this, we can incorporate another tool from the DMN family called BPMN (Business Process Model and Notation).

BPMN is a widely recognized graphical notation language specifically used for modeling processes. The Kogito framework is capable of executing BPMN diagrams as well. DMN and BPMN are complementary tools within the same family and can be of seamless use. DMN can be embedded within BPMN diagrams to provide decision logic. Moreover, the DMN family encompasses additional members, offering a broader range of tools based on specific use cases.


By incorporating BPMN into our solution, we introduced a stateful component to handle delays. Upon initial observation of the delay, creating a BPMN instance allows us to continuously monitor the delay’s development. The customer receives a notification in case of postponement if there is consistent confirmation over certain occurrences.

This notification enables the customer to cancel the visit or wait for the delayed service. By utilizing BPMN in this manner, we allow a more proactive and customer-centric approach to managing delays within the fleet management process.

A BPMN diagram captures all these details:

To integrate the BPMN instance into our system, we store the BPMN instance ID as a property within the destination digital twin. It demonstrates the system-of-systems role of the digital twin, which facilitates the integration of the current fleet state with references to the corresponding business process and decision components.

By associating the BPMN instance ID with the destination digital twin, we establish a link between the real-time state of the fleet and the specific business process and decision-making aspects relevant to that destination.


This elegant implementation serves as a compelling demonstration of how a solution built upon a digital twin, enhanced with business process and decision logic, can significantly improve customer services while also opening up new avenues for service innovation. The flexibility of the digital twin allows for modeling various domains, enabling tailored solutions to meet specific industry needs.

We are committed to pushing the boundaries further by exploring more complex use cases and incorporating cutting-edge technologies into our solution. For instance, we are actively working on integrating generative AI to unleash even greater potential for optimizing fleet management operations.