Introduction: From AI Experiments to Enterprise Intelligence Large Language Models (LLMs) are redefining how enterprises operate, innovate, and scale intelligence across the organization. What started as conversational AI has rapidly evolved into enterprise-grade systems capable of reasoning over vast datasets, automating knowledge work,…
Executive Summary A large US-based enterprise healthcare organization was struggling to keep pace with growing patient volumes, rising operational costs, and increasing regulatory complexity. Despite having multiple digital systems in place, the organization lacked predictive visibility across patient risk, staffing…
Client Industry: Healthcare Client Size: Mid-market healthcare provider Geography: United States Solution Provider: AI-driven, cloud-native technology consulting partner Technology Stack: Azure · AWS · GCP · .NET · Java · React · AI/ML · Data Analytics Platforms Executive Summary A mid-market, US-based healthcare provider…
Healthcare enterprises are at an inflection point. Rising costs, workforce shortages, clinician burnout, and regulatory pressure are colliding with increasing patient expectations for speed, personalization, and digital-first care. At the same time, healthcare leaders are sitting on vast amounts of…
How modern enterprises are evolving from cloud-first to AI-ready ecosystems with intelligent infrastructure, automation and data modernization Table of Contents Introduction – Why AI-Native Cloud matters now In the last few years, cloud transformation has become table stakes for enterprise IT. According…
Optimising AI infrastructure spend while scaling innovation and agility Table of Contents Introduction – The cost challenge of AI workloads In today’s enterprise technology landscape, the surge in AI/ML workloads is placing unprecedented pressure on cloud infrastructure budgets. According to…