Technical Architecture
This document provides a comprehensive technical reference architecture for the Qinfinite platform, covering design, components, and deployment best practices.
System Overview
Qinfinite is an AI-powered IT Operations platform used to monitor, maintain, and optimize Enterprise IT systems. It reduces the dependency on SMEs in retrieving corporate memory and performs automations related to IT Operations with intelligent insights.
The platform is designed for Enterprise IT functions, alleviating complexity, considering criticality, and improving the stability and availability of IT systems.
Key Capabilities
Discovery
- Discover infrastructure assets and their configurations
- Discover application assets and their configurations
- Scan code and discover complexity and dependency components
Visibility
- Visualize IT topology via Knowledge Graph
- Understand IT asset dependencies and complexity
- Visualize critical IT components, their status, and configurations
Observability
- Monitor the state and health of IT assets
- Identify anomalies in logs, events, and metrics
- Monitor business operations in real-time
- Detect and resolve incidents
- Predict and prevent incidents
Automation
- Low-code / no-code automation builder
- AI-powered automation executor
- Pre-built automation repository
Governance
- Assessment of IT assets and their configurations
- Governance of middleware interfaces
- Monitor and control the performance of ML models
High-Level Architecture
Component Reference Architecture
The platform follows a layered architecture with distinct functional domains:
| Layer | Components |
|---|---|
| Channels | Web, API, Mobile, Conversational |
| Users / Personas | Business Users, Application Owners, DevOps/Support Engineers, SREs |
| AutoDiscover | ITSM Discovery, Application Discovery, Infrastructure Discovery |
| AutoDetect | Transaction / Log / Metrics data collection, Classification & Correlation, Anomaly Detection, Predict Incidents |
| AutoDiagnose | Incident Info Enrichment, Root Cause Identification, Solution Identification |
| AutoRemediate | Auto Heal, Incident/Service Resolution, Resolution Validation |
| AutoOptimize / AutoRecommend | Assessment, Capacity Management, Availability Management, Problem Management |
| ML Cockpit | Model Governance & Optimization |
| Infrastructure | Discovery Data Ingestion, Monitoring Data Ingestion, Data Transformation, Knowledge Graph, Rules Engine |
Implementation Reference Architecture
The implementation architecture connects users, enterprise systems, and the Qinfinite platform:
| Component | Role |
|---|---|
| SmartView | User-facing dashboards and portals (ERG Dashboard, Admin Portal) |
| Qinfinite Gateway | Secure entry point connecting customer environments to the platform |
| Qinfinite Remote Agent | Deployed in customer network for secure data collection from on-premise systems |
| Data Pipeline | Discovery data collection, Monitoring data collection, Data Ingestion, Data Transformation |
| Data Store | Encrypted storage (AES-256) for all collected and transformed data |
| Service Catalog | Automation Engine for orchestrating workflows |
| Vault | Secure credential and secret management |
Communication Protocols
| Connection | Protocol |
|---|---|
| User to platform | HTTPS over TLS 1.2+ |
| Platform to enterprise apps | Pull-based HTTPS (Port 443) |
| Remote Agent to platform | HTTPS / TLS 1.2 & 1.3 |
| Client IP access | Whitelisted IPs only |
| Data at rest | AES-256 encryption |
Data Flow
All data collection follows a pull-based model -- the platform pulls data from customer systems rather than requiring customers to push data. This approach:
- Minimizes firewall changes required on the customer side
- Keeps all connections outbound from the Remote Agent
- Reduces the attack surface for customer networks