Applied AI and Data Science
Orchestrate your AI agents to maximize their impact without losing control.
Everyone is experimenting with GenAI. Few companies know how to scale it effectively. We design, develop, and orchestrate your Data Science models and AI agents to deliver reliable results in production — not just impressive demos that don't hold up in real-world conditions.
+150 companies supported
— 5/5 on Google
Benefits / Impacts
What it brings
Concrete results for your organization, measurable within the first weeks of intervention.
01
AI models that solve business problems
Every model we develop addresses a defined business objective: reducing costs, accelerating processes, or improving predictions. No models for the sake of modeling.
02
Orchestrated and controlled AI agents
Your GenAI agents don't run unchecked. We implement safeguards, monitoring, and validation processes that ensure reliable and controlled responses.
03
POC to production, included
We don't just deliver a notebook. We deliver an industrialized system, integrated with your business tools, deployed in production, and supervised. The POC is just a step, not the final deliverable.
04
Leveraging your proprietary data
The true value of GenAI in business is revealed when it works with your data, not generic knowledge. We connect LLMs to your information assets for results specific to your context.
05
Upskilling your teams
Our Data Scientists and AI Engineers work with yours, not in their place. Skill transfer is integrated to empower your team with new techniques.
Chapters
Our Expertise
The skills and expertise we deploy to deliver results in this area.
Tools / Partnerships
How we apply this expertise
We rely on market-leading technologies, always choosing the tool best suited to your context — not the newest or most fashionable.
Amazon Web Services
Google Cloud
Azure
Apache Airflow
Power BI
Amazon Web Services
Google Cloud
Azure
Apache Airflow
Power BI
Amazon Web Services
Google Cloud
Azure
Apache Airflow
Project Types
What we deliver
Actionable deliverables, not just recommendations. Here are the types of projects we undertake in this area of expertise.
01
Business analytics foundation
Complete Data Warehouse or Lakehouse architecture, connected to your sources, with a reliable transformation layer and a BI foundation usable by business teams. Delivered in 3 to 6 months.
02
ML/AI Infrastructure
Complete environment to train, deploy, and monitor your AI models in production: compute, feature store, model registry, serving pipelines, and drift monitoring.
03
FinOps Audit and Optimization
Detailed analysis of your Data and AI cloud costs, identification of immediate optimizations, and implementation of a continuous budget monitoring framework. Visible ROI within the first few weeks.
04
MLOps Industrialization
Transition from manual operations (notebooks, manual deployments) to an industrialized MLOps pipeline: model versioning, CI/CD, automated deployment, and scheduled retraining.
Business Cases
They transformed their analytics and AI infrastructure with us
We don't deliver POCs. We deliver working systems with a measurable impact on our clients' business.
Your questions, our answers
All the answers to understand our approach, our way of working, and what you can expect from our collaboration.
What is an analytics and AI infrastructure?
La GenAI appliquée désigne l'industrialisation de modèles comme GPT, Claude ou Mistral sur les données propriétaires de l'entreprise pour automatiser des tâches métier. Diametral déploie des systèmes RAG sur mesure, des agents autonomes et des copilotes, avec supervision humaine, gouvernance et mesure de la valeur business générée.
How to control AI cloud costs?
Un système Retrieval-Augmented Generation (RAG) combine un modèle de langage avec une base de connaissances propriétaire pour générer des réponses ancrées dans vos documents. Diametral déploie des RAG sur contrats, procédures, catalogues produits ou bases de support, ce qui élimine les hallucinations typiques des LLM généralistes et garantit la traçabilité des sources.
Comment industrialiser un agent IA ?
Industrialiser un agent IA consiste à le sortir du mode démo pour en faire un système supervisé, mesuré et gouverné. Diametral encadre chaque agent avec des garde-fous (filtres de contenu, validation humaine sur les actions critiques, logs complets) et mesure la valeur générée mois après mois pour s'assurer qu'il reste un actif et non une boîte noire.
Why conduct a FinOps audit?
A FinOps audit identifies poorly optimized cloud spending and establishes cost governance by use case. Diametral typically delivers 20 to 40% savings within the first few months on AI environments by eliminating oversized clusters, redundant jobs, and forgotten test environments.
Data Warehouse or Lakehouse: Which to choose?
Data warehouses are suitable for high-volume structured analytics; lakehouses add the ability to manage unstructured data and AI use cases within the same environment. Diametral recommends lakehouses (Databricks, Snowflake) for most large enterprises, as they cover both classic BI and Machine Learning needs.
contact
Is your infrastructure ready for what AI demands?
A 30-minute discussion with a Diametral Data Architect to assess your technical foundation and identify initial optimizations.