Hi Product Hunt! 👋
I'm Johan Gómez, founder of Scalari.
My background is in marketing, business strategy, and analytics, where I've spent years helping B2B companies make better decisions using data. Across companies of different sizes, I kept seeing the same problem: organizations were investing in more software every year, but making decisions wasn't getting any easier.
Instead of having too little data, leaders had too much of it—spread across CRMs, marketing platforms, finance tools, and dozens of other applications that rarely worked together. They spent more time gathering information than acting on it.
That experience inspired me to build Scalari, a Decision Intelligence platform that helps B2B companies connect their existing tools, transform fragmented data into meaningful insights, and support faster, more confident decisions with AI.
I joined Product Hunt to learn from other builders, share our journey, and connect with founders working on the future of AI and B2B software.
I'm especially interested in connecting with people building:
AI-native products
B2B SaaS
Workflow automation
Tools that help companies make better decisions
I'd love to hear your perspective:
What's the biggest challenge you've faced when helping companies turn data into better decisions?
Looking forward to meeting the community and learning from all of you! 🚀
Replies
Great intro, Johan! the "too much data, not enough decisions" problem is exactly right, and it doesn't go away by adding another dashboard.
The biggest challenge I've seen is that the data companies collect mostly tells you what happened, but decisions need you to know what's still true. A metric from last sprint, a capacity plan from two weeks ago, an assumption someone made when they scoped a feature these all look like solid inputs until the moment they go stale, and nothing flags that they did. So teams end up making confident decisions on evidence that quietly expired.
I'm Sean, founder of Everview Ai.
we're building an execution intelligence layer for product and engineering teams. Similar space to yours but from the other end: you're connecting business data to decisions, we're connecting engineering execution data (what teams planned vs. what actually shipped, and why the gap exists) to decisions. Haven't launched yet, but would genuinely love to compare notes the "evidence behind the decision" problem sounds like it shows up in both our worlds.
What's your take on the stale-data piece? Is that something Scalari handles explicitly, or do you focus more on connecting the sources and let the team judge freshness?