Civimatica provides physical AI infrastructure for urban environments, combining privacy-preserving edge sensors with street-level measurement and machine learning models. The company offers instrumented real-world test routes, curated urban datasets, and deployable prediction models that help autonomous delivery and robotics teams validate systems in dense city conditions. Their devices process data locally, extracting only occupancy, flow, and object class information while never capturing faces, plates, or raw images.
Funding
Funding not disclosed
Founders
Product
Problem
Urban AI systems are typically trained on proxies like payment records, scheduled transit data, or simulated streets, meaning models learn an approximation of the city rather than its actual behavior. Autonomous delivery robots and vehicles face a critical validation gap when transitioning from simulation to real-world operation in dense, messy urban spaces where kerbs, cyclists, and double-parked vehicles create unpredictable conditions.
Solution
Civimatica provides a physical AI platform that measures real street conditions through privacy-preserving edge devices and trains models on that measured reality. The company operates instrumented real-street routes where autonomous delivery robots and vehicles can run, fail, and be measured against ground truth. Their devices process frames in memory, extracting only occupancy state, flow and speed bands, and object class information while never storing raw images, faces, or license plates. Models are trained on continuous real-world signal, shipped as APIs or run at the edge on Civimatica devices, and validated against ground truth as the city changes.
Target Audience
Primary customers are autonomous delivery and robotics teams, mobility operators, cities, and enterprises building urban AI systems that need models validated against real-world street conditions.
Features
- Edge devices that measure street space, flow, and movement while processing frames in place and never storing raw images, faces, or plates
- Civimatica Sandbox: instrumented real-street test routes with repeatable scenarios and measured ground truth for autonomous delivery and robotics validation
- Urban state models providing live, queryable data on what streets, kerbs, and corridors are doing, trained on measurement rather than estimates
- Prediction models that forecast street occupancy, flow, and disruption from continuous real-world signal
- Custom models built to client specifications, trained on street-level ground truth that cannot be scraped or simulated
- European-by-construction data handling with data minimisation as a design constraint, ensuring compliance with EU data rules