Myrkat's open, non-intrusive architecture is designed to seamlessly integrate with, and analyze, R&D and production data sources. It is read-only across all integrations (Logs, Git, Slack, PagerDuty, etc.) to ensure zero impact on performance and security. Myrkat's core architectural goal is to transform disparate raw data into a unified, actionable understanding of code-to-production impact by mapping and analyzing complex relationships. The system achieves this by:
Ingesting Formatted Data and processing it into High-Level Data Entities.
Utilizing Agentic AI to add context and generate relevant Metrics/Events.
Storing and mapping all entities, metrics and events to an integrated Graph Database. This Graph DB is an essential component, as it models the complex relationships and dependencies between all system entities—from code commits and developers to use cases, performance metrics, and log events.
Leveraging the generated relationship as a foundation for the Myrkat Framework (including Static Code Analysis and Anomaly Detection) to create a rich Context Framework for deep analysis.
Finally, delivering the insights to the AI Engine, Myrkat's UI, and a Dashboard, enabling R&D teams to instantly detect performance issues, understand code behavior changes, and manage deployment risk based on a holistic, relationship-aware view.
“what changed in this deployment ?"
See the real impact of every release, performance, reliability, and behavior, all in one clear view.
