Clear
Efficience
Secure
Scalable
Self-service
Generative
Low-code
No code
Multilingual
Connect and collect data
Capture data directly from multiple sources:
- PLCs (Programmable Logic Controllers), SCADA
- BMS (Building Management System)
- Data lakes and historians
- Additional sensors
- External data sources
Securely retrieve data through cutting-edge protocols (MQTTS, OPC UA, etc.).
InUse relies on a network of trusted partners for connectivity hardware.
Our recommendation
InUse prioritizes a non-intrusive data collection and connection strategy that is manageable by the end user, ensuring high performance and resilience.
We recommend collecting only the essential data to optimize the deployment of digital services.
Hosting
Collected data is securely stored and organized separately for each client. Raw data is stored and backed up across multiple sites in France. Our hosting provider, certified ISO 27001 and SOC1/SOC2, is part of the Trusted Cloud initiative.
Our platform operates with high availability, ensuring optimal performance and responsiveness.
We deploy a distributed computing architecture and have developed our own services for infrastructure deployment, monitoring, and alerting.
Thanks to our agnostic approach to hosting and our expertise in infrastructure management, we can deploy the solution on a private cloud within our clients’ environments.
Our recommendation
The SaaS approach centralizes expertise and secures intellectual property while making it accessible to each client.
It serves as a key driver for competitiveness, cost optimization, and enhanced security.
Intelligent pipeline
A digital service relies on the transformation of multiple data sources, often coming from diverse origins.
The complexity of aggregating and harmonizing this data is managed through an intelligent data pipeline.
This pipeline automates joins and correlations between different sources, delivering ready-to-use data for business experts—without requiring development skills.
Each pipeline is monitored and optimized to ensure that only the necessary data volume is processed for each service.
Our recommendation
A digital service should be designed based on its business objective, which determines the required data and its sampling frequency.
By applying advanced practices, we ensure optimal accuracy in capturing all physical phenomena while maintaining an efficient and resource-conscious approach to data storage.
Data workspace
InUse adopte une approche modulaire pour le développement et le déploiement des services digitaux à travers des “data workspaces”. Ces espaces de données facilitent la création d’algorithmes de prédictions, d’optimisations et d’alertes grâce à une approche no-code.
Le moteur d’intelligence artificielle intégré offre un large éventail technologique, combinant opérateurs mathématiques, analyse par apprentissage, traitement de signal, analyse fréquentielle et le traitement d’évènements complexes.
Scalables, ces espaces de données s’adaptent à l’ensemble des besoins de digitalisation des fonctions opérationnelles des équipements, des lignes et des bâtiments. Une analyse descriptive transforme les diagnostics de pannes en algorithmes automatisés de détection des défauts, tandis que le machine learning permet la création de jumeaux numériques probabilistes pour identifier et signaler des phénomènes complexes.
Les résultats obtenus dans ces espaces sont une propriété intellectuelle sécurisée et réutilisable, garantissant une capitalisation durable des développements et la compétitivité des services.
Our recommendation
Cette approche modulaire s’adapte idéalement à un parc d’équipements hétérogène, combinant fonctions communes et spécificités.
Chaque équipement peut être associé aux espaces de données correspondant à sa configuration, assurant un développement accéléré et optimisé en termes de coûts.
User portal
Operator and technician engagement is essential—they are the primary users and beneficiaries of this collective digital intelligence, directly contributing to the success of proactive after-sales service.
Their role revolves around two key aspects:
- Enhancing field data
By labeling information collected in the field, they help structure analyses around the symptoms of recurring critical events rather than limiting insights to confirmed failures, which are less frequent. This approach enhances the relevance of predictive models.
They are ultimately responsible for executing the actions suggested by the algorithms. This final step—the “last mile”—is what transforms a digital service into an operational success, where theory becomes reality.
By defining user personas, InUse simplifies the deployment of digital services for target users in a cost-effective and user-friendly way. The user portal is enriched with intuitive dashboards, designed with the assistance of an AI copilot.
Our recommendation
Digital services should be designed to serve field operators—intuitive, clear, and reliable.
The combination of IoT data AI and generative AI enhances the understanding of analyses, ensuring optimal adoption of digital solutions.