Data & Artificial Intelligence

What is the challenge ?

Across all industries, organizations are collecting more data than ever before. However, turning that data into measurable outcomes remains a complex task. Around 80% initiatives fail to go beyond the Proof of Concept phase, not because of a lack of ambition, but because of the difficulty in bridging the gap between raw data and operational value.

The challenge is to manage the entire data lifecycle: collecting the right data, ensuring its quality and accessibility, extracting insights, and deploying scalable solutions that address real business needs.

Success requires more than isolated efforts : it depends on a clear, end-to-end approach focused on concrete processes use cases and results.

Recognized expertise

We help organizations from assessing the current data landscape to implementing effective governance and unlocking business value, by combining strong technical expertise with a pragmatic, use-case-oriented approach.

We bring together Data Engineering and Data Science know-how, along with powerful packaged tools, to solve challenges related to data collection, management, quality, and accessibility. Whether through advanced analytics, visualization, or Artificial Intelligence (AI) when relevant, we deliver solutions designed to serve concrete operational goals.

Our role is not just to build, it’s to create value.

Solutions by

Industry

Our data expertise applies to critical systems across industries. We’ve defined our methods and tools to meet specific operational, regulatory, and technical challenges.

Highlighted

Use cases

Machine Learning (ML), Generative Artificial Intelligence (GenAI), and advanced analytics offer powerful levers to extract value from data, but only when applied to well-defined operational needs.

Whether it’s automating insights, accelerating decision-making, or improving system performance, these technologies must be integrated into robust, scalable, and trusted solutions.

We support our clients across a broad range of challenges, here are three representative use cases where our expertise creates impact:

Predictive Maintenance

We help clients anticipate failures and improve operational efficiency by implementing predictive models in end-to-end MLOps pipelines. This includes building scalable platforms for data and model orchestration, deploying trained models, and monitoring their performance with clear KPIs. We also support continuous R&D efforts to help evolve and optimize models over time.

AI for Engineering

We apply GenAI techniques based on the latest LLMs (Large Language Models) to support engineering teams in managing growing system complexity. From accelerating requirements analysis to automating test case generation and documentation review, these technologies help reduce manual effort, enhance traceability, and improve compliance. By embedding intelligence into engineering workflows, we enable faster, more reliable system validation in complex environments.

Safe AI

AI systems used in safety-critical contexts must be designed, validated, and monitored with rigorous safeguards. We help establish processes to assess and document the safety of Machine Learning models, from high-level design choices to operational deployment. This includes techniques to mitigate both in-distribution and out-of-distribution risks, and structured approaches to align system engineering, data science, and MLOps within a coherent safety framework.