AI-Powered Edge & Enterprise Platform for Connected Operations
A multi-product, cloud and edge-native platform enabling real-time intelligence, secure operations, and scalable enterprise management across remote environments.
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Client Profile
The client is a technology-driven organization focused on building advanced platforms that leverage data, AI, and automation to transform enterprise operations. They deliver cloud-native solutions across areas such as risk management, security, compliance, and operational intelligence.
Engagement Overview
QBurst partnered with the client to design and deliver a connected, multi-product platform integrating cloud, edge infrastructure, AI, and enterprise systems.
The engagement spans platform engineering, AI-driven applications, cloud-native architecture, identity and access management, and observability systems, enabling real-time intelligence and resilient operations across distributed environments.
Engagement Scale & Reach
40+
Member cross-functional engineering team
15+
Interconnected platform modules delivered across AI, edge, cloud, and enterprise systems
100+
Microservices and infrastructure components orchestrated across distributed environments
Multi-cloud and edge deployments supporting real-time telemetry, video analytics, and operational intelligence
Key Engagement Pillars
- Cloud-native, microservices-driven platform architecture
- Edge computing and connected infrastructure enablement
- AI-powered operational and safety solutions
- Centralized identity and access management
- Monitoring and observability for real-time insights
- Scalable enterprise web and application platforms
Business Impact
- Unified architecture reduced platform fragmentation and enabled 3–5x faster onboarding of new services and use cases.
- Centralized observability improved issue detection time by 60% and reduced mean time to resolution (MTTR) by 50%.
- Cloud and edge-native deployments enabled high-volume telemetry and video workloads, with 45% improvement in processing latency compared to traditional architectures.
- Automated analysis and real-time processing reduced manual monitoring efforts by 75%, while enabling near real-time decision-making.
- Centralized identity and access management reduced administrative overhead by 40% and enabled consistent policy enforcement across services.
- Modernized enterprise interfaces improved internal adoption and reduced time-to-insight by 30%.
Key Project Snapshots
01
AI-Powered Safety Intelligence Platform
Objective: Develop an AI-driven solution capable of detecting safety risks in video data across cloud and edge environments, enabling real-time alerts and improving workplace safety.
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Our Role
Designed and delivered a scalable AI platform integrating real-time video processing, cloud infrastructure, and edge deployment capabilities for safety monitoring.
Solution Highlights
- AI processing unit built in Python for detecting PPE compliance, fire hazards, and unsafe actions
- React-based user interface for monitoring, alerts, and reporting
- Spring Boot-powered backend services for API orchestration
- High-volume data streaming managed using Kafka and GPU resources
- Cloud infrastructure implemented using Azure services, including storage, event streaming, database, and Kubernetes
- GitOps-driven automated deployments to self-hosted Kubernetes across distributed edge locations, enabling reliable processing in remote environments
Impact
- Reduced manual safety monitoring efforts by 70% through automated video analysis
- Enabled near real-time incident detection with significantly lower latency (50% improvement)
- Scaled to handle high-throughput video streams across distributed environments
- Improved responsiveness to safety events through automated alerts and reporting
02
Azure Service Onboarding Automation for FedRAMP Compliance
Objective: Design and deliver an automated infrastructure provisioning framework to onboard microservices onto Azure Public and Government Cloud (AGC) in compliance with FedRAMP Moderate requirements — eliminating manual configuration, enforcing security controls consistently, and enabling auditable, repeatable deployments at scale.
Our Role
Architected and implemented a declarative, policy-driven service onboarding platform that provisions all required Azure infrastructure for each microservice across commercial and government cloud environments, integrating GitOps-based workflows, identity federation, and compliance-by-default resource configuration.
Solution Highlights
- Built a centralized, declarative onboarding framework where new services can be added through simple configuration files instead of custom engineering work, making onboarding faster and more consistent.
- Automated the setup of all required Azure infrastructure through reusable OpenTofu/Terraform Infrastructure-as-Code (IaC) templates, including networking, identities, databases, security controls, and access management.
- Designed the platform to work seamlessly across both Azure Public Cloud and Azure Government Cloud using a single shared framework, reducing duplication and operational complexity.
- Implemented GitOps-driven, approval-based deployment workflows so all infrastructure changes are automatically reviewed, validated, and tracked before going live.
- Replaced stored credentials and manual secret management with secure OIDC-based federated identity authentication, significantly improving security and reducing operational overhead.
- Enforced security and compliance standards by default, including private endpoints , centralized secret management, role-based access controls (RBAC), and secure database authentication.
- Isolated infrastructure deployments for each service and environment, ensuring issues in one deployment do not impact other applications or teams.
Impact
- 90%+ reduction in service onboarding time, from days to under two hours, with 30+ Azure resources auto-provisioned per service, including identity, networking, secrets, and databases.
- 150+ manual provisioning steps eliminated per onboarding, replacing fragmented portal-based setup (including access management, network configuration, security rules, and credential provisioning) with a fully automated, configuration-driven onboarding model.
- 100% environment parity across dev, staging, and production, eliminating configuration drift via a single declarative source of truth validated at every deployment.
- 95%+ reduction in infrastructure deployment errors through policy-enforced IaC replacing manual provisioning workflows.
- Eliminated long-lived CI/CD credentials, with full migration to OIDC-based federated identity and removal of stored secrets and key rotation overhead.
- Achieved FedRAMP Moderate compliance consistency across all services, with fully logged, traceable, and audit-ready provisioning actions.
- Zero-dependency service onboarding enabled for engineering teams, allowing new services to be provisioned via configuration after initial platform setup.