Civimatica S.r.l.
Physical AI for the real city.
We build AI models for urban space, run them on our own street-level sensing devices, and give companies the data and ground truth to train and validate theirs. Privacy by design, from the first byte.
01Models meet an unmeasured world
AI is leaving the data centre. The street isn't ready.
Models built for physical space are trained on simulations, scraped proxies and occasional surveys, then deployed onto streets that behave nothing like the data that produced them.
A delivery robot validated in simulation meets a kerb, a cyclist and a double-parked van for the first time in production. An urban model trained on payment records learns who paid, not what the street was doing.
The information exists. It is simply unmeasured, and where it is measured, it is usually collected in ways public space cannot accept.
02What Civimatica does
We build the models, the machines, and the ground truth.
Our own sensing devices measure what happens at street level: occupancy, flow, movement, processed on the device itself.
On top of that measured reality we build and operate our own models. And we open the same substrate, datasets, devices and an instrumented real-street environment, to companies building theirs.
- Our models
- AI for urban space, trained on streets we measure ourselves.
- Your models
- Data, devices and test runs for teams building their own.
03How it works
Sense. Model. Deploy.
Sense
Purpose-built devices deployed in the street measure occupancy, flow and movement continuously. Real signal, not a yearly survey.
Model
Models train on measured urban state to produce prediction and behaviour: what a corridor does next, how a machine should read a kerb.
Deploy
Our models run at the edge on our own devices. Yours get validated on instrumented routes with measured ground truth.
04Offering 01: model access
Civimatica Models
Ready-to-use AI for the city: models that understand streets, kerbs, corridors and movement. You do not build or train the stack; you plug into models trained on streets we measure. Example: a logistics team uses our occupancy model to see which kerbside spaces are free along a delivery route.
- Urban state modelsLive, queryable state of streets, kerbs and corridors, built on measurement, not estimates.
- PredictionWhat a street or kerb will do next, learned from continuous real-world signal.
- Operated by usWe build, train and run the models end to end. You consume the outputs.
05Offering 02: API
Civimatica API
A simple API connection so your systems can ask for street results and receive them automatically. Behind it, our edge devices measure the street and our models turn that into useful answers. You get the answers, not raw camera feeds.
Street answers
Where a parking spot is available, where a pothole is, where street assets are damaged.
Conditions on demand
Occupancy, flow and movement classes, queryable as your systems need them.
Processed, not raw
Devices measure, models interpret. The API returns results, never camera feeds.
06Offering 03: sandbox
Civimatica Sandbox
A safe test space filled with street-state data produced by our models. Run your own models against it first, see how they behave, fix problems early, then move to the real world with more confidence. Example: a robotics team tests its navigation model on our street data before putting a robot outside.
Model-derived street data
Occupancy, flow, movement classes and related urban conditions, already processed for testing.
Test before deployment
See how your models behave on real street patterns before they meet a real street.
Iterate with confidence
Fix problems in the sandbox, then move to the physical world with evidence.
07Offering 04: devices with models
Civimatica Devices
Edge devices that already have our AI running on them. You place them on site; they measure the street and process locally; you receive the useful outputs. The physical layer of the AI infrastructure, not a cloud-only setup. Example: devices on a corridor send live parking and flow results to your operations room.
- Edge inferenceModels run on the device itself. What leaves is already processed street state.
- Outputs, not footageLive results such as parking and flow, delivered to your systems.
- Physical infrastructureDeployed hardware carrying our models, the layer that makes the street measurable.
08Privacy by design
Understanding a city should not mean surveilling the people in it.
01 · On the street
The device measures space, not people
Our sensors look at the street itself: whether a kerb slot is occupied, how a corridor is flowing, how objects move through space. Frames are processed in place, in memory.
Leaves this step
- Occupancy state
- Flow and speed bands
- Object class (car, bike, van)
Never leaves / never stored
- Raw images
- Faces
- Plates
- Anything that names a person
No identities at the edge
Our devices are built to measure the state of the street, space, flow, movement, not who is in it.
Aggregate before it leaves
Data is reduced on the device. What travels to our platform is already stripped and aggregated.
European by construction
Built and operated in Europe under EU data rules, with data minimisation as a design constraint rather than a policy page.
09Who it is for
Robotics & autonomy
Teams that need real streets, real edge cases and measurable ground truth before deployment.
Companies building models
Teams that need urban AI trained on reality, built with our data, our devices, or our test runs.
Cities & operators
Administrations and mobility operators that need evidence, not estimates, about their own streets.
10Why now
Three shifts arriving in the same street.
- 01
AI is moving into physical space
Models are leaving the data centre for streets, robots and vehicles, and physical AI needs physical data that almost nobody measures.
- 02
Autonomy has run out of simulation
The remaining gap for delivery robots and vehicles is real-world validation in dense, messy urban space.
- 03
Privacy pressure is a moat, not a tax
European deployment increasingly requires systems that never collect personal data in the first place. Ours does not.
Let's build on the real city.
Investors, robotics teams, enterprises and cities: tell us what your models need to understand about the street, and we will tell you what we can measure.
hello@civimatica.comCivimatica S.r.l.