Case studies
Automation rule engine for critical infrastructure security platform
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Our client is a leading provider of communication and security solutions for on-field assets. They have an in-house software platform, which provides integrated security management with automatic counter measures as per system settings.
The client solution was built as a threat prediction and resolution system with automatic counter measures as per system settings. It can be integrated with systems like- CCTV, Fiber-based intrusion detection, Intercom, public address, general alarm, radio & status contact.
The client wanted to develop a Rule Engine Workspace on top of their existing solution that could be used by non-technical users to create rules using drag-and-drop operations for inputs, outputs, and functions. They wanted the Rule Engine Workspace to act as a canvas for creating rules as a graphical flowchart. Dynamic Rule Validation and Rule Testing capabilities were also required.
The client wanted to make their existing software solution more user-friendly and accessible for non-technical users. They wanted to create a Rule Engine Workspace that could be utilized by their clients to create rules using drag-and-drop operations. The client needed Codvo.ai to help them develop the Rule Engine Workspace and integrate it with their existing solution.
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Codvo.ai focused on developing multiple design engine automated workflows connected to external IoT devices. We started off with reviewing the execution flow and delivering a POC by integrating the test system for MQTT events. We also worked on delivering CDC (Change Data Capture)enabled events from Postgres with NATS Jetstream as Pub-Sub. Our team proceeded to formulate deployment instructions for Ubuntu based system for deploying microservices. We also worked on understanding whether Camunda would be our final choice for the rule engine and then doubled down on it. We validated the cause-and-effect test cases in Camunda.
We connected Debezium, Cassandra and NATS connector for CDC and build the initial database to store the workflow meta information. Once complete we set up Camunda on client infrastructure and the Camunda cluster for testing and designing the database workflow manager. We then set up the Postgres CDC to NATS in Linux VM and set up a test system to complete performance testing.
BPMN and DMN standards were used to model business processes and decisions within a process. Camunda Modeler was utilized for the development of BPMN and DMN visual modelers, and it was deployed on-prem. We managed the complete decision lifecycle from design to implementation to execution on the platform.
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