Intemic's platform empowers companies to create data pipelines that model and monitor their processes, regardless of complexity. From wastewater treatment to chemical manufacturing and food & beverage, our solution extracts data patterns to build digital twins that provide deep operational insights.
The platform utilizes modular building blocks that can be customized and integrated to represent ontologies and relationships across departments, unit operations, data sources, and value chains involving multiple stakeholders.
These building blocks consist of nodes and edges:
Nodes: Representing actions, objects, or transitions. Each node contains Python-based logic to execute data transformations or simulate physical operations, consuming inputs and generating output results.
Edges: Representing information flow between nodes as datasets, with variables as columns and time-stamped measurements as rows.
Architecture: Intemic's backend is built in python, and it leverages AWS services like S3 for data storage and EC2 for backend processing. For efficient large dataset handling, it integrates with Databricks for managing large amounts of data.
Dockerized: The infrastructure is containerized in a Docker, which can be deployed in different hosts, including on-premise
Real-time streaming operations: The data-processing jobs are orchestrated by an event-driven scheduler, which makes the platform suitable for real-time or high-latency data-processing use cases.
Collaborative environment:
The platform allows multiple users across an organization to share and edit the digital twin within the platform.
Deployment:
The platform allows multiple users across an organization to share and edit the digital twin within the platform.
