Cloud · Azure · Adopt
Practical Generative AI on Azure, From First Experiment to Production
Most businesses know AI matters but aren't sure how to move past a proof of concept. We help identify high-value use cases and build production-grade generative AI solutions on Azure, including the Azure OpenAI Service.
Azure Has the AI Infrastructure — We Build What Runs On It Reliably
Azure provides serious infrastructure for generative AI — the Azure OpenAI Service's access to leading foundation models, Azure Machine Learning for custom model work, and the compute infrastructure to serve it all reliably, often tightly integrated with data already living in the Microsoft ecosystem. But infrastructure alone doesn't get you from a demo to a production system people depend on. That gap requires solid use case discovery, integration with your actual data and workflows, and the monitoring and governance a live system needs.
We approach Azure-based generative AI the way we approach any infrastructure decision: start with the business problem, then build the solution — including the Azure infrastructure to run it reliably — around that.
What Generative AI on Azure Covers
Use Case Discovery
Identifying high-value, technically feasible use cases and assessing data readiness
Solution Design
Architecture spanning Azure OpenAI Service, Azure Machine Learning, or fine-tuned models depending on the use case
Integration
Connecting AI solutions to your existing applications, Microsoft 365 data, and data sources
Deployment Infrastructure
Scalable, secure infrastructure for hosting and serving models on Azure
Data Pipeline & Governance
Reliable data pipelines and access controls supporting the AI system long-term
Who This Is For
- Businesses with a clear problem but unsure whether or how generative AI applies
- Teams with a proof of concept that never made it to production
- Organizations with data on Azure or in Microsoft 365 that isn't yet organized to support AI reliably
- Companies wanting to move on AI without taking on unmanaged compliance or reliability risk
Related Azure Services
Common Questions
Do you build custom models, or use Azure OpenAI Service's foundation models?
Most engagements use Azure OpenAI Service's foundation models via API or fine-tuning, since building a model from scratch is rarely justified. We recommend the right approach based on your specific requirements.
Can this integrate with our Microsoft 365 data specifically?
Yes — integration with Microsoft 365 and broader Microsoft ecosystem data sources is a common and often advantageous starting point for Azure-based AI solutions.
How do you handle the risk of AI generating incorrect output?
Through retrieval-grounded design, evaluation frameworks, and human-in-the-loop review for higher-stakes use cases, depending on how the output is used.