Decision Analytics
Building an AI infrastructure that scales without exploding costs.
Your dashboards are multiplying, your models are consuming more and more resources, and your cloud bill is rising without the value keeping up. We design the analytics and AI infrastructure that supports the rise in demand, with controlled costs and reliable performance.
+150 companies supported
— 5/5 Google reviews
Benefits/Impacts
What it brings
Concrete results for your organization, measurable from the first weeks of intervention.
01
Reliable analytics that your teams trust
More discussions on numbers in meetings. Your indicators are calculated from a single source, with documented and shared calculation rules.
02
Infrastructure that absorbs growth
Your data volume is doubling every year. The architecture we put in place is designed to scale without requiring a redesign at each level.
03
Predictable and optimized cloud costs
We size the infrastructure as needed and set up monitoring mechanisms that avoid budgetary slip-ups. Every euro spent on computing is justified.
04
A technical base ready for advanced AI
Analytical infrastructure is the foundation on which your machine learning models, AI agents, and GenAI systems run. Without a solid base, no model fits in production.
05
Response times compatible with business
Your analytics queries no longer take 20 minutes. Dashboards load in seconds, models infer in real time, and business teams get answers when they need them.
Chapters
Our expertise
The skills and know-how that we mobilize to deliver results on this expertise.
Tools/Partnership
How do we implement this expertise
We build on market-leading technologies, always choosing the tool that best fits your context, not the most recent or the 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
Type of projects
What we deliver
Operational deliverables, not recommendations. Here are the mission formats that we deploy on this expertise.
01
Business analytics foundation
Complete Data Warehouse or Lakehouse architecture, connected to your sources, with a reliable transformation layer and a BI base that can be used by business teams. Deliverable 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 cloud Data and AI costs, identification of immediate optimizations and establishment of a framework for continuous budget monitoring. ROI visible from the first weeks.
04
MLOps industrialization
Transition from artisanal operation (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 do not deliver POCs. We deliver systems that work, with a measurable impact on the business of our customers.
Your questions, our answers
All the answers to understand our approach, how we work and what you can expect from our collaboration.
What is analytics and AI infrastructure?
The analytics and AI infrastructure includes the technical building blocks that allow you to train, deploy and monitor your models on a large scale: data warehouse or lakehouse, feature store, model registry, CI/CD pipelines, and MLOps. Diametral designs this infrastructure so that it scales without redesigns at each level of growth.
How do you control the cloud costs of AI?
Controlling the cloud costs of AI is based on three levers: fair sizing, automating the stopping of unused resources, and optimizing queries. Diamétral carries out a FinOps audit that identifies waste, applies immediate improvements and sets up continuous monitoring so that each euro of computing remains justified.
What is MLOps?
MLOps is the set of practices that automate the life cycle of an AI model, from training to production monitoring. Diametral industrializes your models via CI/CD pipelines, a shared feature store and a centralized model registry, which eliminates manual notebooks and secures each production pass.
Why do a FinOps audit?
A FinOps audit identifies poorly optimized cloud expenditure items and sets up cost governance by use case. Diametral typically delivers 20 to 40% savings in the first few months on AI environments, by eliminating oversized clusters, redundant jobs and forgotten test environments.
Data Warehouse or Lakehouse, what should you choose?
The data warehouse is suitable for high-volume structured analytics, the lakehouse adds the ability to manage unstructured data and AI use cases in the same environment. Diametral recommends the lakehouse (Databricks, Snowflake) for most groups because it covers both traditional BI and Machine Learning needs.
contact
Is your infrastructure ready for what AI requires?
A 30-minute exchange with a Data Diametral architect to assess your technical base and identify the first optimizations.