EXPERTISE

Data Science & AI

Do you want to create predictive value and boost your revenue through artificial intelligence? We design and integrate AI and machine learning models directly into your products and business processes to automate, predict, and go beyond mere analysis.

The finding

AI is a hot topic everywhere, but few organizations know where to start, how to assess the feasibility of their use cases, or how to deploy models that deliver on their promises over the long term. Caught between the pressure to innovate and technical complexity, many AI projects get bogged down before they’ve even proven their value. That’s exactly where our Data Science & AI Practice comes in.

Our support

Our experts work closely with your teams to understand your business challenges before proposing a solution. We support you every step of the way—from identifying your use cases to deploying your models in production—including a proof-of-concept (POC) phase that allows you to validate the value with minimal risk.

What we solve

Have you identified potential AI use cases but aren’t sure where to start or how to assess their feasibility?

We define the scope of your projects, prioritize the right areas, and quickly create prototypes to validate the value before investing.

Are your AI experiments stuck at the POC stage and never making it into production?

We can help you scale your models, from MLOps to integration into your existing products and processes.

Are you looking to integrate generative AI into your products or automate complex business processes?

We design solutions tailored to your specific requirements, your tech stack, and your ethical and environmental considerations.

Our expertise

Machine Learning

Deep Learning

Generative AI

Large Language Models

Operations Research

Statistics

POC IA

MLOps

Our professions

Data Scientist

Machine Learning Engineer

AI Engineer

Research Scientist

Examples of assignments

How we work

We always start by gaining a deep understanding of your business challenges before making any recommendations. There’s no one-size-fits-all approach: every AI solution is tailored to your use cases, your tech stack, and your product goals. 

Our data scientists and AI engineers work closely with all of your product teams—including product managers, developers, data engineers, and data analysts—to ensure that AI is seamlessly integrated into your products and processes, rather than existing as a siloed specialty.

Do you have a project in Data Science & AI?

APPROACH 1

Staffing

A consultant who works closely with your teams to provide targeted expertise on your challenges in modeling, machine learning, symbolic AI, or generative AI.

APPROACH 2

AI Consulting & Proof of Concept

Support from one or more experts to define, prototype, and validate your low-risk AI use cases before proceeding with a larger-scale deployment. 

APPROACH 3

Customized workshop / training

A customized workshop lasting from half a day to three days to identify your AI use cases, assess their feasibility, or raise your teams’ awareness of the challenges of artificial intelligence.

They give us trust

BPCE
Radio France
France tv
Tarkett
SNCF Connect
Pathé
Engie
BNP Paribas
Samsung
Marionnaud
Groupama
Maisons du monde
Renault Digital
Schneider
Boursorama
Airbus
Infomil
Safran
Carrefour
Geev
Arkea
EDF
Betclic
CDiscount
Matmut
BPCE
Radio France
France tv
Tarkett
SNCF Connect
Pathé
Engie
BNP Paribas
Samsung
Marionnaud
Groupama
Maisons du monde
Renault Digital
Schneider
Boursorama
Airbus
Infomil
Geev
Safran
Carrefour
Arkea
EDF
Betclic
Cdiscount
Matmut

From experimentation to real-world value

When it comes to data transformation, it all starts with the quality of the foundation. Without a robust architecture and reliable pipelines, no analytics project or AI model can deliver on its promises. Data is a strategic asset, but you still need to be able to trust it.
Data Engineering is precisely that foundation: designing, building, and maintaining the infrastructure that enables data to be collected, transformed, and made available to those who need it, at the right time and in the right format.

An iterative, low-risk approach

Before building anything, we work with you to identify the use cases that offer the most value and involve the least complexity. This prioritization is what allows us to get started quickly, demonstrate value early on, and keep your teams engaged over the long term.

Identifying and prioritizing your AI use cases

Rapid prototyping and field validation

Industrialization and production deployment

Skill transfer and empowering your teams

Goal: Solutions that deliver on their promises beyond the proof of concept. Each solution is designed to integrate with your existing products and processes, not to replace them.

A broad range of technologies under our control

From operational research to machine learning, and from deep learning to large language models, our experts cover the entire spectrum of artificial intelligence. This technical depth enables us to address a wide variety of use cases: prediction, classification, recommendation, content generation, and process automation.

We remain open-minded about the tools we use and select the technologies best suited to your requirements, your tech stack, and your goals.

Green AI and Ethical AI as standards

AI has lasting value only if it is responsible. We are strongly committed to Green AI and Ethical AI, and we incorporate principles of ethics and sustainability into our work from the very beginning: model transparency, reducing our carbon footprint, protecting privacy, and preventing bias.

We are therefore committed to developing appropriately scaled solutions for more resource-efficient AI, with a focus on efficiency and relevance.

Because a well-designed model is, above all, a model that is useful, well-managed, and respectful of both users and resources.

AI Supporting Products and Business Units

Our data scientists and AI engineers work closely with all relevant stakeholders—including data engineers, data analysts, product managers, developers, and business teams—ensuring that AI serves a shared vision rather than operating as a silo of expertise. 

A PO/PM can help identify, define, and prioritize use cases; the designer creates a user-friendly interface; and the data scientist develops, tests, and refines a relevant, effective, and efficient model. Once validated, the model is deployed into production by an MLOps or DevOps team. This team of experts enables the management of a product that incorporates an end-to-end AI model and ensures its successful operation in production.

This cross-functional approach ensures that the models developed are adopted, understood, and maintained over the long term. The result: enhanced products, optimized processes, and teams that become more self-reliant.

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