business logic
3 stories filed under business logic on Beyond Market Intelligence. The newest of them: “Let AI handle dashboard visuals while you keep control of the business logic”, “Build portable serverless logic without sacrificing native cloud power”, and “Your data pipeline is the real reason your AI is confidently wrong”. Building dashboards in Power BI shouldn't mean building every visual from scratch. Vendor lock-in feels like a price you pay for using the cloud, but Elena van Engelen thinks you can have it both ways. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every business logic story on Beyond Market Intelligence, newest first.
Let AI handle dashboard visuals while you keep control of the business logic
Building dashboards in Power BI shouldn't mean building every visual from scratch. Let AI handle the visuals and measures while you keep control of the business logic, relationships, and data normalization. It's a practical division of labor that frees you to focus on what matters. For deeper insight into how data teams structure these workflows, our guide on pairing data scientists with business experts offers a useful perspective. That's the kind of collaboration that drives real results.
Build portable serverless logic without sacrificing native cloud power
Vendor lock-in feels like a price you pay for using the cloud, but Elena van Engelen thinks you can have it both ways. In her presentation, she lays out how Clean Architecture, Spring Cloud Function, and Gradle modules keep business logic portable across AWS and Azure. The payoff is a live demo deploying Kotlin services with Terraform CDK. It's a practical path to flexibility. For more on reducing cloud constraints, check out our piece on stateless deployments with the Model Context Protocol.
Your data pipeline is the real reason your AI is confidently wrong
The failure that doesn't look like a failure is the one that quietly undoes your AI system. You ship it, stakeholders sign off, and months later, the answers drift wrong, not because the model changed, but because the data behind it didn't move with the world. That's not a retrieval problem; it's a data engineering one. Correctness, freshness, consistency, lineage, these are the checks that matter, and most pipelines still monitor for completion, not truth. If you're chasing model swaps, you're solving the wrong puzzle.