[Co-founder here of a start-up that provided monitoring / metadata analytics for cloud warehouses] My unsolicited $0.02 - I think your approach is spot on. As

[Co-founder here of a start-up that provided monitoring / metadata analytics for cloud warehouses] My unsolicited $0.02 - I think your approach is spot on. As a company, you will never have one consistent data set and metrics if you keep building an individual model for each user / use case / etc. And I've seen the explosion of tables and models in real-time. They just keep growing. And how do you even know that the question you're asking in your dashboard is pulling the information from the correct table? I've yet to see a data team that didn't have to deal with drift. Plus, there's a real cost of storing all these stale tables that nobody is looking at anymore. What your product is doing is what I see companies already trying to accomplish themselves [somewhat]. For the leading companies when it comes to working with data, the warehouse today is already the source of truth, with one dimension table that points back to the SaaS tool / dashboard via an S3 bucket. So the SaaS tool itself is really only the last mile and visualization layer. Run the model, create the table, offload the table to S3, point the tool to the S3 bucket with the table. Update every 4 hours, etc. dbt wins in that world. (and I assume you're using something like dbt under the hood of narrator.ai?) That approach is already commoditizing the SaaS tool down to the visualization layer and the opinionated way of displaying data. But that still means there's at least one model per tool, use case, etc. with one table - and you still don't see the entire journey of the user, that's something you either have to create for a single specific use case, or cobble it together ad-hoc. If instead you have one table that has it all - you can move soooo much faster with data, and take out all the friction that comes from having disparate data sets. narrator.ai wins in that world. Blinkist is Berlin is following a very similar approach to what you guys have built. This deck is a few years old, but I think the approach described will resonate with you: https://www.slideshare.net/SebastianSchleicher/tracking-and-business-intelligence https://www.slideshare.net/SebastianSchleicher/tracking-and-... If I had to look into my Crystal Ball, I think one of your GTM challenges will be to convince existing data teams that everything they've built is somewhat redundant. On the flipside, I can see the same data teams say "OMG, finally!". I'm curious to hear the customer reactions so far. I'm very excited about this product! I wouldn't be a direct user with my current role, but FWIW, I can share the bruises I got from working in this market. Would love to hear more!