Engineering a custom Python and Bicep automation framework to migrate mission-critical edge AI workloads to a secure, FedRAMP-compliant government environment.
A leading US-based edge computing startup providing full-stack AI and connectivity solutions for remote environments.
The client needed to migrate 40+ applications to Azure Government Cloud to meet stringent FBI, NSA, and DoD compliance mandates.
We engineered a modular automation toolchain—Infra-Deployer and Service-Deployer—using Python, Azure Bicep, and Helm to facilitate seamless Government Cloud migration.
Operating at the frontier of edge computing, this US-based startup delivers a full-stack platform uniting connectivity, compute, and AI. Their technology is specifically designed to operate in rugged, remote, or connectivity-challenged environments where real-time data generation is critical.
Expanding from commercial to government cloud presented significant technical and compliance-related hurdles.
We partnered with the client to implement an extensible Python-based framework that automates the entire lifecycle of infrastructure and Kubernetes services. The solution effectively eliminated manual YAML maintenance by using Jinja2 templates to dynamically generate environment-specific configurations.
The Framework Consists of Two Core Engines:
The solution follows a "Learning and Detecting" methodology. By using statistical data mining to create empirical profiles, the system detects subtle changes in system behavior weeks before a manual inspection would. This provides the maintenance and procurement teams with more time for corrective action planning.
Client Profile
Challenges
QBurst Solution
Technical Highlights
Implementation Approach
Impact