Why this platform exists.
Most companies already have the data they need. What they don’t have is a way to see across it. We built Zandara so a mid-sized organization can have a live, honest model of its own operations without hiring a data team or replacing anything it already runs.
Why we built this
We’ve spent our careers in enterprise technology building products, leading security programs, and working with operational data at scale. Throughout that time, we kept seeing the same thing: companies sitting on valuable data they couldn’t use because it was scattered across systems that were never designed to talk to each other.
The AI boom made this worse, not better. Every vendor got an “AI-powered” feature, but none of them work across systems. The real intelligence, the patterns that emerge when you connect disparate data sets, stayed buried.
We built Zandara to close that gap. Not as a consulting engagement, and not as another dashboard bolted on top. As a platform that reads what you already have, resolves the real things behind the records, connects them, and keeps the result live.
No new platforms to adopt. No 18-month transformation program. Just a live model of your organization, built on your own data.
The Team
Parisa Bazl
Co-Founder
16 years in enterprise SaaS product design, spanning product strategy, UX architecture, and go-to-market execution for data-intensive platforms. Parisa has led product design for complex analytical tools used by operations teams and executive leadership alike. She brings a deep understanding of how data becomes actionable intelligence, and how to deliver it in a form that operational leaders actually use.
Chad Vaughn
Co-Founder
Air Force veteran with 10+ years leading cybersecurity and offensive security operations. Chad has managed high-stakes data environments where precision, security, and speed are non-negotiable. His background in threat intelligence and adversarial analysis brings a rigor to Zandara’s analytical approach that conventional data consultancies lack: pattern recognition trained on environments where missing a signal has real consequences.
Your data is trying to tell you something.
See it on a sample of your own data. No new infrastructure. No preparation on your side.