Decision Analytics
Build an AI infrastructure that scales without blowing up costs.
Your dashboards are multiplying, your models consume more and more resources and your cloud bill keeps climbing without a matching increase in value. We design the analytics and AI infrastructure that supports scaling, with controlled costs and reliable performance.
+150 companies supported
— 5/5 on Google
Benefits / Impact
What it delivers
Tangible results for your organization, measurable from the very first weeks of the engagement.
01
Reliable analytics your teams trust
No more debating the numbers in meetings. Your KPIs are calculated from a single source, with documented and shared calculation rules.
02
Infrastructure that absorbs growth
Your data volume doubles every year. The architecture we put in place is designed to scale without a rebuild at every stage.
03
Predictable, optimized cloud costs
We right-size the infrastructure and set up monitoring mechanisms that prevent budget overruns. Every euro of compute is justified.
04
A technical foundation ready for advanced AI
Analytics infrastructure is the foundation on which your machine learning models, AI agents and GenAI systems run. Without a solid foundation, no model holds up in production.
05
Response times that match business needs
Your analytical queries no longer take 20 minutes. Dashboards load in seconds, models run inference in real time and business teams get answers when they need them.
Chapters
Our expertise
The skills and know-how we bring to produce results in this area of expertise.
Tools / Partnerships
How we put this expertise into practice
We rely on market-leading technologies, always choosing the tool best suited to your context — not the newest 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
Project types
What we deliver
Operational deliverables, not recommendations. Here are the engagement formats we deploy in this area of expertise.
01
Business analytics foundation
A complete Data Warehouse or Lakehouse architecture connected to your sources, with a reliable transformation layer and a BI foundation business teams can actually use. Delivered in 3 to 6 months.
02
ML/AI infrastructure
A 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 an ongoing budget management framework. ROI visible from the very first weeks.
04
MLOps industrialization
Moving from ad hoc operations (notebooks, manual deployments) to an industrialized MLOps chain: 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 systems that work, with a measurable impact on our clients' business.
Your questions, our answers
Everything you need to understand our approach, how we work and what you can expect from working with us.
What is an analytics and AI infrastructure?
An analytics and AI infrastructure brings together the technical building blocks needed to train, deploy and monitor your models at scale: data warehouse or lakehouse, feature store, model registry, CI/CD pipelines and MLOps. Diametral designs this infrastructure so that it scales without a rebuild at every growth stage.
How can you control the cloud costs of AI?
Controlling AI cloud costs relies on three levers: right-sizing, automatically shutting down unused resources and optimizing queries. Diametral conducts a FinOps audit that identifies waste, captures immediate savings and sets up ongoing monitoring so that every euro of compute remains justified.
What is MLOps?
MLOps is the set of practices that automate an AI model's lifecycle, from training to production monitoring. Diametral industrializes your models with CI/CD pipelines, a shared feature store and a centralized model registry, eliminating manual notebooks and securing every production release.
Why conduct a FinOps audit?
A FinOps audit identifies poorly optimized cloud spending and sets up cost governance by use case. Diametral typically delivers 20% to 40% savings on AI environments within the first few months, by removing oversized clusters, redundant jobs and forgotten test environments.
Data Warehouse or Lakehouse: which should you choose?
A data warehouse is suited to high-volume structured analytics; a lakehouse adds the ability to handle unstructured data and AI use cases in the same environment. Diametral recommends the lakehouse (Databricks, Snowflake) for most large enterprises, as it covers both traditional BI and Machine Learning needs.
contact
Is your infrastructure ready for what AI demands?
A 30-minute conversation with a Diametral Data architect to assess your technical foundation and identify the first optimizations.