build
Building Native AI systems and teams.
You know what you want to automate or optimize with AI. It is now necessary to build it, integrate it into your existing system and put it into production. We develop Data and AI systems that work beyond the POC.
+150 companies supported
— 5/5 on Google
Challenges
The challenges you face
You have identified what you want to build, but your POCs are not going into production. Deadlines are exploding, skills are lacking and AI remains disconnected from the job.
01
Your POCs never go into production.
The model works in a test environment, but as soon as it needs to be integrated into existing systems, everything gets complicated. Data, infrastructure, compatibility issues. The project is stagnant.
02
Your tech teams don't have the AI skills in-house.
Your developers master your IS, but not machine learning, MLOps, or model orchestration. Recruiting takes too much time, so does training.
03
Development times are exploding.
The project was supposed to last 3 months, it is now 9. Specifications are changing, the architecture was wrong, and no one knows when the system will be available.
04
AI is developed in silos, disconnected from the business.
The Data Science team built a powerful model on paper, but business teams don't use it because it doesn't fit into their daily tools.
Response
Our approach
We provide services for both consulting and structured projects, with a commitment to delivering results.
Technical and functional framework
Together with your teams, we define the scope of the MVP, the target architecture and the criteria for success. No specification tunnel: a tight framework to get started quickly.
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 effects.
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 on a daily basis by business teams.
Start-up and stabilization
We don't deliver a prototype. We deliver a system tested, documented and deployed in production, with the necessary monitoring to guarantee its reliability from day one.
Benefits / Impacts
What you gain
An AI system in production, deadlines met, native integration into your IS, and skills transfer to your internal teams.
01
An AI system in production, not one more POC. You are moving from experimentation to an operational tool used by your teams.
We don't deliver a prototype. We deliver a system tested, documented and deployed in production, with the necessary monitoring to guarantee its reliability from day one.
02
Deadlines met thanks to a well-established agile methodology. Short framing, fast iterations, delivery on time.
A dedicated team iterates in short cycles with your business and tech teams. Regular demos, continuous adjustments, zero tunnel effects. Planning is a commitment, not an optimistic estimate.
03
Native integration into your existing tools. AI is not just another application to open: it fits into the workflows that 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 on a daily basis by business teams, without changing their habits.
04
An integrated transfer of skills. Your internal teams increase their skills during the project, not after.
Our Data Scientists and Engineers work with yours, not for them. Documentation, pair programming, code reviews: the know-how stays in your organization when the mission is over.
05
A system designed to evolve. The architecture is designed to support scale-up if the project must be deployed in other entities or on other perimeters.
We anticipate Scale as soon as we build. Modularity, code standards, repeatable pipelines: if success is confirmed, the extension does not require rebuilding everything.
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 the Build phase at Diametral?
The Build phase transforms the Design target into operational AI systems delivered in production, not into a prototype. Agile squads (Data Scientists, Data Engineers, Architects) develop each brick in short sprints with regular demos and a continuous transfer of skills to your internal teams.
How do you go from an AI POC to production?
Going from a POC to production requires three simultaneous projects: technical operationalization (MLOps, monitoring, CI/CD), functional framework (use scenarios, SLA, governance), and change management. Diametral manages these three projects in parallel during the Build phase to avoid the classic pitfall of POCs that never make it beyond the presentation stage.
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 keep the project on track and avoid the tunnel vision often seen in poorly managed AI projects.
Does Diametral train my teams during the project?
Yes, skill transfer is integrated into every Diametral Build mission by default. Your Data Scientists, Engineers, and Product Managers work in pairs with our experts throughout the project, gradually taking control and having complete documentation to operate the system independently after delivery.
What are the deliverables of the Build phase?
The Build phase delivers four elements: a robust technical and functional framework, 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 based on a fixed price or contract, with contractual SLAs.
contact
Building an AI system? Let's talk about it.
Describe your project. A Diametral expert will get back to you within 24 hours with an initial evaluation.