Parking data an AI can actually use.
An agent cannot reason across nine vendors' portals. Parkeze puts every pay app, meter, gate, camera, sensor, permit and citation in one ontology with one permission model, so an agent can read the whole operation and act through it, with a person approving anything that matters.
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Why the data layer comes first
One vocabulary
A session from a pay app and a stay from a camera are the same kind of fact. An agent asks once, not once per vendor.
Actions, not just answers
Opening a gate, issuing a citation or requesting a tow all go through one action registry, so an agent can propose them and a person approves.
Automations
What happens next is written down: warn, wait, cite, tow. The automation engine runs it on the ontology, step by step, and every run is recorded.
Evidence behind every number
Answers trace back to the events that produced them, so an AI's summary can be checked against the record.
Shares to your models
Send live data to your warehouse or your own models through a share you approve and can revoke.
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Permissions before prompts
An agent connects as a person, with that person's role and nothing more. Actions that change the world wait for approval, and every run records who asked, who approved and what changed.
| Interfaces | REST, webhooks, MCP |
| Auth | OAuth, scoped API keys |
| Approvals | Per action |
| Audit | Every run |
| Data out | Shares, export |
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