A lot of people ask, “Can’t this be done with publicly available data? Can’t GenAI just crawl the internet and get this info?”

Here’s the truth: you cannot build a decision-support system for entertainment using public or scraped data alone.

What makes Vitrina powerful isn’t just the scale—7M+ structured entities, updated daily—it’s the depth of invisible knowledge: the hidden preferences, internal workflows, biases, and unwritten rules that govern how companies and teams actually operate.

When Globo in Brazil looks for international licensing partners, their content, genre fit, pricing thresholds, and even internal SOPs matter.
When Netflix, across 14 of their major content hubs, explores co-pros in emerging markets, it’s not just about who’s available—it’s about who they’ve worked with before, which execs have approval power, and what their commissioning behavior looks like over time.

Vitrina captures this because it was trained with both structured data and behavioral context—from ongoing partnerships, org charts, role histories, project metadata, and direct market signals.

This is industrial intelligence, not content indexing.

It’s the reason why animation studios in Canada, VFX firms in South Korea, and global media-tech vendors are using Vitrina—to qualify opportunities they simply couldn’t reach through search or CRM plugins.

You can’t do this with surface-level data. You need depth, domain logic, and an understanding of how the industry actually moves behind closed doors. That’s what we’ve built into Vitrina’s operating system.