Benefits of using IoT-TICKET AI
For Business Leaders
Accelerate outcomes
with AI-powered intelligence
IoT-TICKET AI empowers your organization to move from insight to action—faster. By embedding advanced AI capabilities directly into the IoT-TICKET platform, you eliminate the need for costly integrations or standalone solutions. This means faster time-to-market, reduced operational friction, and greater ROI from your digital initiatives. Whether your goal is to optimize processes, reduce downtime, or deliver new data-driven services, IoT-TICKET AI helps you unlock value from your data with minimal overhead. It’s part of your existing subscription, ready to scale as your business grows.
For Technology Decision-Makers
Modern, flexible AI infrastructure,
no heavy lifting required
IoT-TICKET AI is built for developers, data scientists, and IT architects who need agility without complexity. The platform offers a ready-to-use, low-code/no-code environment for running ML models, enriched with real-time and historical data. Skip the hassle of building and maintaining microservices—our AI services framework supports seamless model orchestration, external model integration, and data harmonization across your systems. Fully compatible with modern AI ecosystems and scalable across deployments, IoT-TICKET AI ensures your technology stack remains efficient, interoperable, and future-ready.
onnx runtime
and models
IoT-TICKET enables fast, low-code AI with built-in ONNX support, real-time data enrichment, and seamless integration—making scalable model deployment easy for all users.
python
sdk
Python AI SDK enables seamless analytics with support for explorative analysis, external model execution, and real-time data integration—empowering users to apply advanced AI with familiar tools.
Data interoperability
Mass Data Export enables seamless, large-scale data extraction with flexible triggers, delivery options, and formats—empowering users to integrate, monitor, and analyze data efficiently across systems.
Edge
AI Computing
Edge AI Computing delivers real-time insights at the source, reducing latency and boosting efficiency. Local video processing and AI-driven support enable faster, and smarter decisions.
Data refinement
and harmonisation
IoT-TICKET turns raw telemetry into structured insights with automated aggregation and hyper-functions, delivering clear metrics and deep analytics for smarter decisions.
Computer
VIsion
Computer Vision offers real-time object recognition with edge processing, customizable AI, and NVIDIA integration—enabling secure, efficient, and scalable visual insights for smarter operations.
LLM Based
AI Assistant
IoT-TICKET’s Natural Language AI delivers fast, intuitive insights from IoT data—no coding needed. With multilingual support and direct API access, it simplifies analytics and decision-making.
Machine learning
models & Services
IoT-TICKET offers real-time ML Services for instant actions and scalable ML Models for deep analysis—delivering fast insights, seamless integration, and easy cross-team sharing.
ONNX runtime
and models
ONNX (Open Neural Network Exchange)
The IoT-TICKET platform introduces a standardized format for representing deep learning models, promoting consistency when working across different tools and platforms. With its native ONNX runtime seamlessly integrated into the IoT-TICKET Core, users can efficiently execute machine learning models without requiring extensive external resources or configurations.
A low-code/no-code ONNX dataflow block further simplifies the process by enabling users to call ONNX models with no coding experience. This user-friendly approach ensures that even those without deep technical expertise can execute models with ease. Additionally, the platform offers registered model access, allowing users to utilize pre-trained models or manage custom models directly within the system for greater flexibility.
The integration of real-time and historical data enrichment enhances the platform’s capabilities, enabling ONNX model results to augment existing data streams or create new AI-driven time series. These enriched data sets empower deeper insights and support more accurate decision-making processes. Moreover, users can trigger actions based on model outputs, such as raising alarms, sending notifications, or issuing commands to field devices, enabling responsive and automated workflows.
IoT-TICKET ensures seamless integration of ONNX model outputs with existing platform tools for further data processing. Its robust scalability and performance, enabled by the efficient ONNX runtime, make it well-suited for large-scale deployments, ensuring reliable execution and optimal resource utilization. This comprehensive approach delivers advanced AI capabilities directly within the IoT-TICKET ecosystem, catering to a wide range of user needs.
Python AI SDK
for data scientist
Unleash the power of real-time AI with IoT-TICKET’s seamless Python-driven analytics and integration.
The IoT-TICKET platform includes robust Python AI SDK, providing a seamless environment for leveraging advanced analytics within the system. SDK capabilities are designed to support a wide range of applications, enabling users to access and utilise AI-driven insights directly in their workflows.
One of the core strengths is its support for explorative data analysis, allowing users to perform in-depth investigations into their data to uncover patterns, trends, and valuable insights. The platform also facilitates external job configuration, enabling the setup and execution of external AI and ML models directly from IoT-TICKET. This integration streamlines workflows and simplifies the process of incorporating sophisticated models into existing systems.
IoT-TICKET offers advanced data harmonization capabilities, ensuring consistency and accuracy by aligning data across different datasets within data frames. It also supports real-time data integration, empowering users to leverage live data streams for dynamic AI and ML applications, enabling responsive and actionable insights.
By being built in Python, the platform aligns with the familiar Python AI ecosystem. Data scientists can seamlessly utilize existing Python frameworks and tools, which accelerates the adoption process and allows users to achieve results faster by working with well-known libraries and technologies. This combination of accessibility, integration, and advanced analytics positions IoT-TICKET as a powerful tool for AI-driven innovation.
Data interoperability
mass data export
Empower data-driven decisions with seamless, scalable, and real-time Mass Data Export.
The IoT Platform’s AI capabilities include Mass Data Export, a powerful feature designed to enable large-scale data exports with ease and efficiency. This feature ensures that users can seamlessly extract and manage extensive datasets for analysis and integration into their workflows.
Data exports can be triggered in a variety of ways to meet diverse needs. Event-driven and continuous streaming options allow data exports to occur automatically whenever the digital twin receives new information, ensuring a consistent and up-to-date flow of data. Additionally, users can manually trigger exports for specified time periods, providing greater control and flexibility over the timing and scope of data extractions.
The platform supports flexible delivery channels, enabling data to be forwarded to external storage systems, data lakes, or any destination preferred by data scientists. This adaptability ensures compatibility with various tools and systems, enhancing the ease of integration. To cater to different analytical requirements, the platform supports multiple export formats, including CSV and the data scientist-favored Parquet file format, making it suitable for a range of use cases.
A dedicated user interface further enhances the experience by allowing users to monitor and follow data streams in real time. This transparency and control ensure that users can track the progress of exports and address any issues proactively. By providing easy access to large datasets and facilitating efficient analysis, the Mass Data Export feature empowers data scientists and business users to derive insights quickly and make agile, data-driven decisions.
Data refinement
and harmonisation
Unlock deeper insights with automated data aggregation and advanced hyper-functions in IoT-TICKET.
IoT-TICKET’s Data Aggregation and Hyper-Functions capabilities are at the core of its powerful stream processing engine, designed to automatically interpret and process incoming device data. By aligning raw telemetry data with the platform’s data model, IoT-TICKET ensures consistency and structure, enabling seamless integration and analysis.
The platform takes data aggregation to the next level by automatically calculating and organizing statistical data from both incoming telemetry and existing time-series data. Known within IoT-TICKET as “Statistical Data,” this process categorizes data into various time periods, such as daily, weekly, monthly, quarterly, or yearly intervals. Key metrics, including minimum, maximum, average, count, and sum, are calculated for each period, providing a clear and actionable overview of trends and performance over time.
For deeper analysis, IoT-TICKET offers hyper-functions—specialized tools for examining time-series data in detail. These advanced functions enable users to uncover complex insights and patterns hidden within their datasets, providing a richer understanding of system behavior and performance. By combining automated data aggregation with the analytical power of hyper-functions, IoT-TICKET empowers businesses to make informed decisions and derive maximum value from their IoT data.
Machine learning
Unlock Real-Time Intelligence and Scalable AI with ML Services & Models
Machine Learning (ML) Services and ML Models provide powerful, complementary capabilities for organizations looking to maximize the value of their data. ML Services are optimized for real-time, synchronous execution within data flows—perfect for scenarios where immediate analysis and response are required. Whether it’s triggering alarms, issuing device commands, or sending real-time notifications, ML Services ensure swift action based on live data.
On the other hand, ML Models offer asynchronous execution, making them ideal for heavier, more complex AI workloads. Deployed on scalable analytics platforms like Databricks, ML Models handle time-intensive algorithms without interrupting operational processes. Their results are automatically integrated into digital twins, ensuring that insights flow seamlessly back into the system for ongoing optimization.
Both ML Services and ML Models support cross-organization sharing, enabling collaboration across teams and subscriptions through an intuitive admin UI. This fosters reusability, consistency, and rapid innovation throughout your ecosystem. Additionally, ML Models are designed for effortless data integration and scalability, allowing businesses to extract value from their existing datasets and grow AI capabilities in step with their evolving needs.
Together, these capabilities empower your organization to react instantly when it matters—and dive deep when it counts. Whether you need real-time decision-making or comprehensive AI analysis, ML Services and Models ensure your data is always working for you.
Get
started
Want to learn how you can accelerate your business creating market ready apps and services stunningly fast with IoT-TICKET?
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