scale
Scale your transformations across the enterprise.
Your AI solution works on a limited scope. Now it needs to be rolled out across the whole organization, without multiplying costs, losing performance or spending two years on it.
+150 companies supported
— 5/5 on Google
Challenges
The challenges you face
Your AI solution works on a limited scope, but every attempt to extend it starts from scratch. Costs multiply, data quality varies and governance can't keep up.
01
Your pilot works, but it remains confined to one team.
The model runs in production on a limited scope. Everyone is convinced of its value. But when it comes to rolling it out to other entities, subsidiaries or countries, no one knows how to go about it.
02
Every new rollout starts from scratch.
The architecture wasn't designed for multiple entities. Each extension requires an effort almost equivalent to the initial development. Costs and timelines explode with every iteration.
03
Data quality varies from one entity to another.
The model performs well on the pilot team's data, but data from other entities is structured differently, incomplete or of variable quality. Performance degrades at scale.
04
Governance isn't keeping up with growth.
As the number of users and models grows, compliance, traceability and control issues become more critical. And no one has structured any of this.
Our answer
Our approach
We work both as advisors and on structured projects, with a commitment to results.
Scalability audit
We assess the existing architecture, data flows and business processes to identify what holds up at scale and what needs to be redesigned. No massive rollout on fragile foundations.
Infrastructure industrialization
We adapt the technical architecture to support scaling: pipeline automation, model standardization, implementation of MLOps and multi-entity deployment environments.
Data harmonization
We structure the data repositories, standardize formats across entities and put in place the quality controls needed for models to perform consistently everywhere.
Gradual rollout and governance
We roll out entity by entity, with a governance framework that grows with the scope: access rights, decision traceability, performance tracking by model and by entity.
Benefits / Impact
What you gain
Sharply reduced marginal deployment costs, consistent performance across the entire scope, governance that holds and multiplied ROI.
01
Plummeting marginal deployment costs. Each new connected entity costs a fraction of the first thanks to an industrialized architecture.
We automate pipelines, standardize models and set up multi-entity deployment environments. What took months for the pilot now takes only weeks for each subsequent extension.
02
Consistent performance across the entire scope. Models work just as well in the Lyon subsidiary as in the Brussels one.
We harmonize data repositories, standardize formats across entities and deploy the quality controls needed to ensure every model performs reliably, whatever the local context.
03
Governance that holds. Regulatory compliance, traceability and monitoring are built in from the start, not added as an afterthought.
As the scope grows, compliance and control issues become more critical. We deploy a governance framework that evolves with the number of users, models and entities.
04
Autonomous local teams. Each entity uses the AI systems independently, without relying on a central team for every action.
Training for local teams, tailored documentation and identification of internal champions for each rollout wave. The goal: every entity operational without daily support tickets.
05
A multiplied return on investment. The value created by the initial pilot is replicated across the whole organization, turning a local success into a global competitive advantage.
Scale is when ROI changes dimension. A model that saved €200K on a single scope generates several million once deployed group-wide.
Your questions, our answers
All the answers to understand our approach, how we work and what you can expect from working with us.
What is AI Scale in the enterprise?
Scaling AI means rolling out a solution that works on a pilot scope to all the entities, subsidiaries or countries of a group. Diametral Scale industrializes the infrastructure, harmonizes data quality across entities and sets up scalable governance so that each rollout doesn't start from scratch.
How do you roll out AI across all countries?
Diametral scales AI in three steps: a scalability audit of the pilot architecture, standardized MLOps industrialization, then a gradual rollout by entity with a common governance framework. This approach sharply reduces the deployment cost per new subsidiary and guarantees consistent performance.
Why does scaling make costs explode?
Scaling makes costs explode when the pilot isn't designed for scale: each entity redevelops its connectors, multiplies models and duplicates cloud resources. Diametral tackles this problem from the Build phase by imposing a shared architecture, a single feature store and standardized CI/CD pipelines.
What is MLOps at scale?
MLOps at scale refers to the set of practices that make it possible to deploy and supervise dozens or hundreds of AI models in parallel. Diametral equips your teams with a centralized model registry, automated drift monitoring and multi-environment CI/CD pipelines to keep control of a growing fleet.
How do you ensure data governance when scaling?
Data governance in the Scale phase rests on three pillars: a single quality framework applied to every entity, a shared data catalog and a consistent access policy across countries. Diametral rolls out this framework gradually to avoid operational disruptions and ensure AI Act and GDPR compliance across the group.
contact
Ready to move from pilot to group standard?
Describe your current context. A Diametral expert will work with you to assess the shortest path to a scaled deployment.