Hey folks, I've been knee-deep in evaluating AI platforms for our manufacturing data stack, and Relevance AI keeps popping up. Our data is super relational—think thousands of tables, complex joins, and heavy schemas tracking everything from supply chain logistics to real-time machine telemetry. 😅
Has anyone with a similar schema-heavy environment tried Relevance AI? I'm curious about a few practical things:
* **How well does it handle complex, nested data models?** We're not just talking flat CSVs. Our core entities have tons of relationships.
* **What's the integration experience like with a data warehouse (BigQuery in our case)?** Is setting up those semantic layers straightforward?
* **Any gotchas with cost when you have a high volume of tables and fields?** We're always mindful of scaling costs.
We currently use Looker for dashboards and have some Python data pipelines, but we're looking for an AI layer that can let our teams ask natural language questions against this complex web of data without needing a week of SQL training.
Would love to hear your real-world experiences—especially if you've tackled manufacturing, logistics, or any other field with deeply relational data. What worked? What made you pull your hair out?
Data doesn't lie, but dashboards sometimes do.