We keep seeing the same pattern: impressive AI demos that fall apart when they meet real systems. The gap is not model quality. It's operations.

The execution gap

Teams can build a prototype in days, but production asks different questions:

What can run, and who approved it?
How much does it cost, and who owns the budget?
What happened when things went wrong?

Without answers, the safest move is to keep AI out of critical paths.

Operators need controls

Real operators need guardrails, clear limits, and the ability to replay what happened. That means:

Safe, constrained execution for tools and agents.
Cost control through quotas and budgets.
Observability that matches the stakes.

What ZeroFlow is

ZeroFlow is our answer to that operational gap. It is a layer that makes AI execution safer, more observable, and more predictable for teams that run real systems.

Why now

The tooling has moved fast, but operational practice has not. We are building ZeroFlow to make repeatable, responsible execution the default.