The Story Behind the Solution
On a routine data consulting assignment, it became obvious that our teams needed a safer, easier way to access and work with data from anywhere. ZeusDB began as a practical fix for secure, fast access to data from anywhere in the world, grounded in solid structure and clear controls. It grew into the database we wished we had every day.
What Drives ZeusDB
A practical origin, a clear focus, and a belief that reliable data layers unlock great products.
Built Because We Needed It
We started as hands-on data consultants wrestling with disparate data, fragile handoffs, and the usual friction of working across teams and locations. ZeusDB became our answer. A dependable data layer you can trust.
What We Set Out To Do
We built ZeusDB so teams could securely store, query, and share data without friction. The goal has always been the same: structure, security, and speed behind the work people already do.
Why Databases Matter
Databases are foundational, quietly powering almost everything. They are essential infrastructure and deserve thoughtful design, clear controls, and reliable performance.
From a practical fix to a modern database
A simple idea became a dependable data layer. The milestones below show how ZeusDB grew with real work and real constraints.
Real world work highlighted an opportunity
A routine client trip in the US surfaced a straightforward truth: teams need secure, easy access to data wherever they are. We responded with early prototypes centered on structure, security, and speed.
ZeusDB is founded and our first site goes live
The goal was clear from day one. Give teams a dependable data layer that supports real projects and real users.
First customers!
Early projects replaced risky spreadsheets with structured storage and dependable access. Individuals also adopted ZeusDB as secure cloud database space they could query easily from anywhere.
Structured storage, SQL access, and workflow tooling
Real projects shaped the product. We focused on reliability, governance, and a smooth path from prototype to production.
Vectors and ANN algorithms
We begin researching how data will be stored for AI applications. Focus areas include vector indexing, ANN search, and metadata filtering, with early prototypes informing performance and scale.
Research, managed cloud, and enterprise capabilities
We are investing in AI-first database research and a managed cloud with security, governance, and dependable day-two operations.
Built for teams that work everywhere
Regional performance and future data residency options are part of the plan moving forward. Pins highlight our primary regions for low-latency deployment, with more to follow.
EnglandChinaAustraliaUSANew ZealandBrazil- England
- China
- Australia
- USA
- New Zealand
- Brazil