Infrastructure as Code (IaC) and Deployment Automation
For all Knovvu Product Family applications, infrastructure and application deployments are fully managed and automated through Infrastructure as Code (IaC):
- All infrastructure configurations are defined in code; changes are made by updating the code rather than manual interventions.
- Reduces the risk of human error, such as misconfigurations.
- Historical changes are version-controlled, enabling easy troubleshooting and rollback if needed.
- Deployment environments (test, pre-production, production) can be replicated consistently, ensuring uniformity across all stages.
- Manual configuration is never used, ensuring reliability and traceability.
Frequent Releases
Knovvu teams follow agile principles, releasing updates every two weeks for Conversational Analytics and Virtual Agent products:
- Short cycles allow rapid customer feedback and faster innovation.
- Frequent, smaller updates reduce risk and improve stability.
- Deployment and automated testing pipelines ensure that every release is reliable and validated before reaching production.
Version Updates
Knovvu leverages Kubernetes’ rolling update strategy to deploy new versions without service interruption:
- New pods (microservice instances) are scheduled on available nodes.
- Kubernetes waits for the new pods to be healthy before removing old pods.
- This ensures continuous availability during updates, with no downtime required for version changes.
In case of exceptions, any expected downtime in a deployment is automatically detected in the development pipeline, and customers are informed in advance about planned maintenance windows to ensure transparency and minimize impact.
Centralized Logging and Observability
With a distributed microservices architecture, monitoring and observability are critical to understanding how the system behaves and ensuring reliability.
- Centralized Logging: All logs from every service are collected in one place, so engineers can see the full history of what the system is doing. This helps identify errors or unusual behavior quickly.
- Performance Metrics: The system collects measurements such as CPU usage, memory usage, request rates, and response times. These metrics show how well services are performing and whether they are under heavy load.
- Request Tracing: When a user request passes through multiple services, the system tracks it from start to finish. This allows engineers to see where delays or failures occur, even across many components.
These observability practices enable:
- Faster detection and resolution of errors
- Identification of performance bottlenecks
- Improved uptime and reliability
Additionally, operational dashboards provide visibility into both business metrics (e.g., how many conversations have been ingested, analyzed, or indexed) and system metrics (e.g., resource usage, scaling activity). This comprehensive view ensures that teams can monitor and manage every service effectively.