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 data analysis to generate measurable business results.
The finding
Artificial intelligence 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.
This is precisely where our Data Science & AI Practice comes in, with an approach focused on data, tools, and real-world applications.
Our support
Our data scientists 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 the POC phase, which allows you to validate the value of your solution with minimal risk. We leverage our expertise in data science, machine learning, Python, programming, and analysis to ensure the success of every project.
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 still in the proof-of-concept phase and never making it into production?
We support you in scaling your models, from MLOps to integration into your existing products, web applications, 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, using the right machine learning and artificial intelligence tools.
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
As part of the management team of an e-commerce site for electronics and household appliances, a consultant was called in to improve the purchasing and post-purchasing customer experience. Analysis of sales KPIs, NPS, customer service activities and user feedback to define a strategy and roadmap, and proposal of a Discovery methodology.
Working for a customer in the supply chain sector, our team, comprising two Product Managers and a Product Designer, was dedicated to the delivery and discovery of their applications. Our intervention was also aimed at guiding the customer towards a more product-centric approach (frequent delivery, OKR, product vision, design process, post-implementation follow-up).
As part of the Product teams of a player in the sports betting industry, our Product Manager leads a multi-profile team: Tech, QA, Design. He is involved in identifying and prioritizing Product initiatives in the context of the creation of a new Offers platform. He leads the Delivery activity in an environment with strong dependencies between Product squads.
In an agritech startup developing autonomous robots, our Product Manager structured the Product organization to support its ramp-up. The mission began with a co-constructed 3-5-year product vision, which was then translated with the teams. A diagnosis of 20 key themes enabled us to measure product maturity and prioritize 33 concrete recommendations. The intervention laid down the fundamentals: clarification of roles, adoption of OKRs, Definition of Done, framing of tests, structuring of rituals. The result: faster decisions, better coordination, smoother development cycles and enhanced performance.
As part of the digital department of France's leading radio group, our Product Management consultant oversaw the design, implementation and roll-out of a new program schedule management tool for all the group's channels. The mission began with an in-depth Discovery phase (user interviews, benchmarking of other media practices, "make or buy" arbitration), and continued with the definition of a clear Product vision, the elaboration of a structured roadmap, the delivery of an operational MVP, and then the progressive roll-out to each channel.
Within the digital department of France's leading radio group, a consultant led the design, implementation, and rollout of a new program schedule management tool for all of the group's channels. The project began with an in-depth discovery phase (user interviews, benchmarking of other media practices, "make or buy" decision-making), followed by the definition of a clear product vision, the development of a structured roadmap, the delivery of an operational MVP, and then gradual deployment across each channel.
How we work
We always start by gaining a deep understanding of your business challenges and your current data 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 your entire team—including product teams, 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. We bridge the gap between business, data, development, analytics, and expected outcomes.
Do you have a project in Data Science & AI?
APPROACH 1
Staffing
One of our data scientists will join your teams to provide targeted expertise on your challenges in modeling, machine learning, symbolic AI, or generative AI. We can assign a data scientist, AI engineer, or Python developer based on your needs. This is the ideal opportunity to advance the careers of your in-house employees by having them collaborate with seasoned data science experts.
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. We assess data quality, potential models, and expected outcomes.
APPROACH 3
Customized workshop / training
A customized workshop lasting from half a day to 3 days to identify your AI use cases, assess their feasibility, or raise your teams’ awareness of the challenges of artificial intelligence. We can tailor the training to focus on Python, machine learning, programming, or data analysis tools. This hands-on training helps participants build new, fundamental skills in machine learning.
They give us trust
From experimentation to real-world value
Many organizations have launched AI projects. Few have succeeded in turning them into a sustainable operational asset. The reason is often the same: models built without sufficient business context, proof-of-concepts that never make it into production, and teams that don’t embrace the tools.
At 5 Degrés, we approach AI as a product: designed to be useful, adopted, and maintained over the long term.
An iterative and low-risk
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 spectrum of mastered technologies
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. We can integrate Python, machine learning libraries, analytics tools, or solutions tailored to your web applications.
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, reliable, and respectful of both users and resources. We ensure that the results are understandable, actionable, and useful to business users.
AI for Products and Business Lines
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.
FAQ - Data Science & AI
There is no universal threshold: the required volume depends on the complexity of the task, the number of variables, and the quality of the annotation. A simple prediction problem can be handled with just a few thousand well-labeled observations. Conversely, a large volume of noisy or poorly documented data can never compensate for a lack of quality.
Barely 5% make it past this stage. The causes are rarely technical: use cases not tied to a business decision, production data that differs from that of the prototype, a lack of monitoring and retraining mechanisms, and, above all, a lack of adoption efforts among end users. A POC validates feasibility, not a path to industrialization.
Traditional machine learning uses historical data to learn how to make predictions or classifications: churn scoring, fraud detection, and recommendations. Generative AI produces new content (text, code, images) using large-scale pre-trained models. The two address distinct problems and are not interchangeable.
MLOps refers to the practices that enable a model to remain in production: data and model versioning, training automation, pre-deployment testing, performance monitoring, and drift detection. It is this framework that distinguishes a model capable of long-term operation from a prototype whose relevance silently deteriorates.
There are four key points to consider: Is the decision or task in question clearly defined? Do the necessary data exist, are they accessible, and are they of sufficient quality? Is the required level of performance achievable? Can the result be integrated into an existing process? A well-defined POC allows these questions to be answered with minimal risk before any significant investment is made.
By sizing the model to actual needs rather than state-of-the-art capabilities: a smaller, specialized, and well-trained model is sufficient for the majority of business use cases. This is complemented by limiting training cycles, optimizing inferences, and selecting appropriate infrastructure. This frugal approach also reduces operating costs.
The data scientist explores, models, and validates: they seek out the method that addresses the business problem and demonstrates its relevance. The ML Engineer industrializes this model: data science, performance, scalability, and integration into the production pipeline. The AI Engineer tends to focus on solutions built using existing models, particularly in generative AI, where the work centers on application architecture and orchestration rather than research. The boundaries between these roles vary from one organization to another. In a small team, a single role often covers the entire cycle, from prototyping to production deployment.
In the vast majority of business cases, starting with an existing model is sufficient and significantly less expensive. Training a model from scratch is only justified for very specific data, strict confidentiality requirements, or performance needs that no available model can meet. In between these two extremes, there are several levels of customization: enriching the context provided to the model, connecting it to your own document sources, or fine-tuning it on a targeted dataset. A useful approach is to test the simplest option first, and only increase the complexity if the results are insufficient.
Bias rarely stems from the algorithm itself; rather, it originates from the training data, which reflects historical imbalances within the organization or the market. Preventing bias therefore begins early on, by examining the representativeness of the data and explicitly identifying sensitive variables—including those that serve as indirect proxies for them. Next comes measurement: evaluating the model’s performance by subpopulation rather than in aggregate, since a satisfactory overall accuracy can mask significant discrepancies. Finally, there is monitoring, because a model that is unbiased at launch can become biased as the data evolves.
Three levels come together. First, scoping: identifying the data that is actually necessary for the use case, since most projects use more data than they need. Next, processing: anonymizing or pseudonymizing whatever can be before any training takes place. Finally, the infrastructure: host the model in a controlled environment rather than sending business data to a third-party service—a critical consideration with generative AI. The most common risk remains the unregulated use of consumer-grade tools by teams, which often goes unnoticed by management as long as no usage rules have been established.
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