Off-the-shelf software-as-a-service (SaaS) tools offered no viable path forward. They lacked the configurability and integration depth that a telecom delivery model of this scale and complexity required. The client needed something purpose-built: a custom Azure-native platform that could unify governance, enhance transparency, and deliver real-time insights across thousands of concurrent projects and hundreds of delivery partners.
The urgency was real. According to a 2024 Sitetracker survey, 59% of telecom executives still rely on spreadsheets to manage projects, and nearly half report difficulties collaborating with third-party systems. This client was determined not to stay in that category.
Solution
The team at Brillio built a modular, Azure-native platform grounded in a domain-driven microservice architecture aligned to the client’s specific business domains. The front end was developed in ReactJS and integrated with the client’s component design framework, shaped through collaborative wireframing workshops that brought product, design, and business teams into the same room from day one.
The backend ran on Azure Functions and .NET Core, with Entity Framework handling data interaction. Azure MySQL managed the data layer, and Azure Key Vault secured credential and configuration management. Power BI dashboards were embedded directly within the application, giving teams real-time visibility into work package velocity, effort realization, demand-supply gaps, cost variances, and risk clusters without switching tools.
Azure Kubernetes Service (AKS) clusters and Logic Apps orchestrated background processing and workflow automation. Azure Storage Queues handled hold/replay transaction scenarios. Single sign-on (SSO) and role-based access control (RBAC) ensured that every user saw exactly what their role required, nothing more, nothing less.
Governance shifted from reactive logging to structured risk management. Risks were defined as threats or opportunities, linked directly to delivery milestones and teams, and semi-mandatory post-milestone surveys created a continuous improvement loop. The platform also integrated RAG-based large language models (LLMs) and AI agents that could take contextual, multi-action decisions on project milestones, work items, and orders.
Deployment followed a phased strategy combining design thinking and agile execution. Azure DevOps pipelines enabled zero-downtime releases, and legacy data migration was completed without business disruption.
