Business cases

Client cases & references

Since 2018, we have been building solutions with our clients that change and simplify their day-to-day work.

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Data Hub design and implementation

A transport operator had multiplied Data initiatives without results. No centralization, no data culture, aborted projects. We started the subject from scratch: strategy, infrastructure, concrete use cases and team acculturation. At the output: an operational DataHub, 4 dashboards in production and 7 Data Science models deployed.

Forum analysis

A pharmaceutical player needed to understand the real problems of patients with Sjögren's syndrome — not those of clinical trials, those that patients express to each other. We built the NLP system that can extract trends, themes, and weak signals from thousands of forum discussions.

Document classification tool

The client's Data Scientists reinvented the wheel for each text classification project: same pre-processing steps, same models, same recoded functions each time. We have developed a generic Python package, interpretable and accessible to non-specialists, which is now used as standard on several projects.

Prediction of delays

A rail transport company needed to improve the accuracy of its delay predictions to better inform travelers and optimize traffic regulation. We took over and improved their existing Deep Learning model, reaching the goal of 30% improvement and deployed in production in real time.

Research Support Assistant

Finding relevant information in a sea of documents takes a considerable amount of time. We developed Kairos, a search assistant that enriches queries, crawls sources, identifies the most relevant content, and reveals thematic links between documents automatically.

Face analysis for product recommendations

A luxury cosmetics player wanted to offer its customers a personalized skin diagnosis directly in store: a photo, a score, identified criteria and a tailor-made product recommendation. We designed the Deep Learning models that make this experience possible, which are now deployed in stores and have resulted in a scientific publication.

FAQ

Your questions, our answers

All the answers to understand our approach, how we work and what you can expect from working with us.

In which industries has Diametral already supported clients on AI and data projects?

Diametral works across a wide range of industries where AI and data deliver real business value. Our client references include:

- The pharmaceutical industry (support for new drug discovery)
- Pharmaceutical distribution (supply chain optimization)
- Insurance (crop yield prediction for drought insurance)
- Luxury cosmetics (skin scoring and analysis with explainable predictions)
- Large consumer goods groups (in-depth analysis of customer needs)
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As well as projects related to trend detection on social media.

Each project is tailor-made to address a concrete business challenge.

Can you give us an example of a business case where AI went beyond traditional methods?

Yes, for example in the case of a pharmaceutical laboratory: manually searching for new molecular combinations across millions of medical publications had reached its limits. Diametral built a dedicated AI system that quickly identifies potentially patentable combinations, significantly accelerating the drug discovery process.

How does Diametral turn social media data into a competitive advantage for its clients?

In a market where consumer behavior changes very quickly, one of our clients needed to anticipate trends rather than react to them. We developed a platform that turns social media “noise” into clear, actionable signals, enabling the client to make faster, better-informed strategic decisions.

What concrete results can you expect from an AI/data project with Diametral?

Results vary depending on the challenge, but our clients typically achieve:

Solutions that move from Proof of Concept to successful industrialization (e.g. a customer needs analysis platform deployed across all French entities of a major group)
Operational transformation (end-to-end supply chain optimization, crop yield prediction making a new product insurable)
Greater transparency and explainability of AI models (as with skin scoring in cosmetics, usable in-store and on mobile)

The goal is always the same: to build tools that genuinely change teams’ day-to-day work and create lasting business value.

Vue aérienne d'un marais avec de petits cours d'eau sinueux traversant des zones de végétation brune et des berges sableuses.

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