Organizations wanted to leverage AI capabilities but lacked clear guidance on integrating machine learning
models with existing low-code applications.
Francisco Silva
Partner & Head of Delivery and OutSystems ·· 1 min read
Practical integration patterns connecting AI services with low-code platforms, covering model deployment,
data pipelines, and user-facing AI features. We built reference implementations for sentiment analysis,
document classification, and predictive maintenance using Azure ML models consumed through OutSystems REST
integrations. The guide covers model versioning, A/B testing strategies, and fallback mechanisms to ensure
graceful degradation when AI services are unavailable. Teams can now add AI capabilities to existing
low-code applications in days rather than months.
OutSystemsAzure MLAI IntegrationLow-Code
About the author
Francisco Silva
Partner & Head of Delivery and OutSystems
Francisco designs and builds applications end to end — infrastructure, data, logic, UI. He gets
his hands dirty across the full stack, and after years of doing it, he knows exactly where the
real problems hide. Since 2019, he's also been a certified trainer, delivering everything from
beginner courses to deep technical tracks.
