build
Build AI-native systems and teams.
You know what you want to automate or optimize with AI. Now it needs to be built, integrated into your information system and put into production. We develop Data and AI systems that work beyond the POC.
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Challenges
The challenges you face
You've identified what you want to build, but your POCs don't make it to production. Timelines explode, skills are lacking and AI remains disconnected from the business.
01
Your POCs never make it to production.
The model works in a test environment, but as soon as it has to be integrated into existing systems, everything gets complicated. Data, infrastructure, compatibility issues. The project stalls.
02
Your tech teams lack in-house AI skills.
Your developers master your information system, but not machine learning, MLOps or model orchestration. Recruiting takes too long, and so does training.
03
Development timelines are exploding.
The project was supposed to last 3 months; it's now at 9. Specifications keep shifting, the architecture wasn't the right one, and no one knows when the system will be available.
04
AI is developed in a silo, disconnected from the business.
The Data Science team has built a model that is powerful on paper, but business teams don't use it because it doesn't fit into their everyday tools.
Our answer
Our approach
We work both as advisors and on structured projects, with a commitment to results.
Technical and functional scoping
Together with your teams, we define the MVP scope, target architecture and success criteria. No endless specification phase: tight scoping to get started fast.
Development in agile sprints
A dedicated team (Data Scientists, Data Engineers, Data Architects) iterates in short cycles with your business and tech teams. Regular demos, continuous adjustments, zero tunnel effect.
Integration into your ecosystem
The AI system is designed to work within your existing infrastructure, not alongside it. APIs, connectors, data flows: everything is integrated so that AI is used daily by business teams.
Go-live and stabilization
We don't deliver a prototype. We deliver a system that is tested, documented and deployed in production, with the monitoring needed to guarantee its reliability from day one.
Benefits / Impact
What you gain
An AI system in production, deadlines met, native integration into your information system and skills transferred to your in-house teams.
01
An AI system in production, not just another POC. You move from experimentation to an operational tool used by your teams.
We don't deliver a prototype. We deliver a system that is tested, documented and deployed in production, with the monitoring needed to guarantee its reliability from the very first day of operation.
02
Deadlines met thanks to a proven agile methodology. Short scoping, rapid iterations, on-time delivery.
A dedicated team iterates in short cycles with your business and tech teams. Regular demos, continuous adjustments, zero tunnel effect. The schedule is a commitment, not an optimistic estimate.
03
Native integration into your existing tools. AI isn't one more application to open: it fits into the workflows your teams already use.
The system is designed to work within your infrastructure, not alongside it. APIs, connectors, data flows: everything is integrated so that AI is used daily by business teams, without changing their habits.
04
Built-in skills transfer. Your in-house teams upskill during the project, not after.
Our Data Scientists and Engineers work with yours, not in their place. Documentation, pair programming, code reviews: the know-how stays within your organization at the end of the engagement.
05
A system designed to evolve. The architecture is built to support scaling if the project needs to be rolled out to other entities or scopes.
We anticipate Scale from the build stage. Modularity, coding standards, reproducible pipelines: if success is confirmed, extending it doesn't require rebuilding everything.
Your questions, our answers
All the answers to understand our approach, how we work and what you can expect from working with us.
What does the Build phase involve at Diametral?
The Build phase turns the Design target into operational AI systems delivered in production, not as prototypes. Agile squads (Data Scientists, Data Engineers, Architects) develop each component in short sprints with regular demos and continuous skills transfer to your in-house teams.
How do you move an AI POC into production?
Moving from POC to production requires three simultaneous workstreams: technical industrialization (MLOps, monitoring, CI/CD), functional scoping (usage scenarios, SLAs, governance) and change management. Diametral handles these three workstreams in parallel during the Build phase to avoid the classic case of the POC that never leaves the PowerPoint deck.
How long does it take to build an AI system?
Building a Diametral AI system takes 4 to 12 weeks depending on the complexity, volume and level of integration required. Intermediate deliverables are demonstrated every two weeks to secure the trajectory and avoid the tunnel effect typical of poorly managed AI projects.
Does Diametral train my teams during the project?
Yes, skills transfer is built into every Diametral Build engagement by default. Your Data Scientists, Engineers and Product Managers work in pairs with our experts throughout the project, gradually take over and have complete documentation to run the system autonomously after delivery.
What are the deliverables of the Build phase?
The Build phase delivers four elements: tight technical and functional scoping, an operational AI system with monitoring and documentation, a scalable architecture ready for the Scale phase, and a handover plan with your teams. The engagement is on a fixed-price basis or managed time-and-materials, with contractual SLAs.
contact
An AI system to build? Let's talk.
Describe your project. A Diametral expert will get back to you within 24 hours with an initial assessment.