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PredictOps Public Safety

Planning & AI
Anticipate risk
optimize response

Developed in partnership with fire departments and the FEMTO-ST laboratory, PredictOps dynamically models risk evolution, rescue demand, and the optimal deployment of resources.

Territorial engineering support: POA.

Contact our Safety Unit
Methodology
15+
Crossed Sources
Weather, traffic, population...
SDIS 25
Reference Model
Main development partner
FEMTO-ST
CNRS Validation
AI Laboratory (UMR 6174)
95%
Simulation Accuracy
Backtesting on past crises
The Story

From spatial data to operational coverage

The modeling of flow concentration points (geomarketing) follows the same mathematical laws as anticipating rescue demand.

01

Field Need: SDIS 25

Faced with a steady increase in the number of interventions linked to aging and climate crises, the Doubs Fire Service sought a probabilistic approach to redeploy its resources fairly.

02

Modeling: SAD & FEMTO-ST

The public CNRS FEMTO-ST laboratory and data scientists set up "spatial machine learning" algorithms. We integrate historical data to reveal patterns invisible to the naked eye.

03

Decision Support

Today, the tool supports major policy decisions: departmental planning, merging rescue centers, and redeploying duty staff in an impartial and unassailable way.

Methodology

Temporal & Spatial Continuum Analysis

1

Risk Mapping

Dynamic analysis and forecasting of population trends over 5 to 10 years based on local urban planning orientations.

2

Scenario Simulation

We calculate the real impact on your isochrone curves and team workload of any merger or center closure.

3

Volunteer Potential

We determine the actual pool of potential Volunteer Firefighter candidates around your operational basins to focus your recruitment.

4

Artificial Intelligence

Beyond classic mapping analysis, neural networks cross temporal data, weather impact, and historical accidentology.

PredictOps Public Safety FAQ

Tout ce que vous devez savoir sur nos solutions.

What is PredictOps for Public Safety?
It is a spatialized Machine Learning algorithm designed to anticipate risks and optimize the operational coverage of Fire and Rescue Services within a given territory.

Which services do you historically work with?
PredictOps was developed in close partnership with the Doubs Fire and Rescue Service (SDIS 25) in France, with whom we test and validate our field intervention models.

Can the algorithm simulate the closure of a station or center?
Yes. The tool can model the impact of closing or merging rescue centers on response times, the utilization of surrounding centers, and the potential degradation of risk coverage (emergency isochrones).

What is the scientific validation of the model?
The algorithmic engine is backed by research from the FEMTO-ST laboratory (UMR 6174 CNRS), one of the most recognized institutes in France for artificial intelligence and predictive systems.

What data do you use to define risk?
We cross-reference internal data (intervention history, equipment) with massive geolocated databases (weather, urban planning, road networks, mobile telephony for seasonal population) to generate dynamic risk maps.

Do you take into account territorial changes (urban sprawl)?
Yes. We project demographic development and urbanization trends over 5 to 10 years to help you size and position your future local or central stations.

Do predictions cover EMS and Fire?
The model covers all types of interventions. Particular attention is paid to Emergency Medical Services (EMS), which follow very specific geographic clustering patterns linked to population aging or medical deserts.

Does the tool manage the availability of volunteer personnel?
We can model potential volunteer recruitment areas around stations based on the socio-demographic characteristics of neighborhoods, in order to geographically target your engagement campaigns.

Is PredictOps Public Safety a software to install?
PredictOps works as a strategic consulting mode or via the provision of interactive cloud dashboards, thus avoiding heavy deployment on the rescue service's IT infrastructure.

How does pricing work?
Our support is provided on a quote basis (public tender or direct agreement depending on amounts). It depends on the size of the territorial group, the history of data to be processed, and the expected deliverables.

Evaluate the model for your service

Our engineers present proven use cases and model the impact of AI on your risk coverage.

Contact our Engineering Unit