Reliable Firebase apps built to scale

We start with a fast audit of product context, codebase and business priorities.

Then we run a shared backlog with short iterations and frequent deliveries.

Yes. We audit the current stack, fix critical issues and restore delivery flow.

The objective is to stabilize quickly without freezing your roadmap.

Yes. We act as an extension of your team with transparent technical steering.

Decisions, backlog and rituals are shared end to end.

Firestore requires a different modeling approach than relational databases: we favor denormalization and simple queries over joins.

We shape your data model around your read/write patterns to avoid hidden costs and performance limitations.

Firestore and Storage rules are our first line of defense: they are versioned, tested and audited like business code.

We pair client-side rules with Cloud Functions validation for sensitive operations.

Yes, Firestore scales horizontally to millions of users. Limits usually come from modeling choices rather than the platform itself.

We anticipate operation costs and composite indexes to keep the bill predictable under load.