Civimatica
← Civimatica

01Offering: our models

Civimatica Models

AI for the physical city, trained on streets we actually measure.

Talk to us about a model

02The problem

A model is only as real as its data

Most urban AI is trained on proxies: payment records, scheduled transit, simulated streets. The model ends up learning a model of the street, not the street. Ours learn from the thing itself, measured continuously by our own devices.

03What we build

Models grounded in measured reality

  • Urban state models

    Live, queryable models of what streets, kerbs and corridors are doing, trained on measurement, not estimates.

  • Prediction models

    What a street will do next: occupancy, flow and disruption, learned from continuous real-world signal.

  • Custom models

    Models built for your machines and your spec, trained on street-level ground truth that can't be scraped or simulated.

04Who it is for

Teams that need models that hold up on the street

Autonomous delivery & robotics teams

Mobility operators & cities

Enterprises building urban AI

05How it works

Measure, model, deploy, validate

  1. Step 1

    Measure

    Our own devices produce continuous street-level signal across instrumented routes.

  2. Step 2

    Model

    We train on that measured reality, not on a simulated approximation of it.

  3. Step 3

    Deploy

    Models ship as APIs or run at the edge on our devices, inside the city they describe.

  4. Step 4

    Validate

    Behaviour is checked against ground truth on real streets, then re-checked as the city changes.

06Get involved

Put a model on a real street

Tell us what your system needs to understand about the city.

Talk to us about a model