Product Delivery
Measure what your AI really brings in each month in euros.
Everyone is experimenting with GenAI. Few companies know how to industrialize it. We design, develop, and orchestrate your Data Science models and AI agents so that they produce reliable results in production, not impressive demos that don't hold up.
+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
AI models that solve business problems
Each model we develop meets a defined business objective: reduce a cost, accelerate a process, improve a prediction. No model for the sake of modeling.
02
Orchestrated and controlled AI agents
Your GenAI agents are not working freewheeling. We put in place safeguards, monitoring and validation circuits that guarantee reliable and controlled responses.
03
The transition from POC to production, included
We don't ship a notebook. We deliver an industrialized system, integrated into your business tools, deployed in production and monitored. The POC is only a step, not the deliverable.
04
Exploitation of your proprietary data
The real value of GenAI in business is when it works on your data, not on generic knowledge. We connect LLMs to your information assets for results specific to your context.
05
An increase in the competence of your teams
Our Data Scientists work with yours, not for them. The transfer of skills is integrated so that your team gains autonomy on new techniques.
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 work with all frameworks and platforms on the market. The technological choice is dictated by your use case, your confidentiality constraints and your existing infrastructure, never by a tool preference.
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
Industrialized predictive model
From framework to production: development, validation and deployment of a machine learning model integrated into your business processes. Customer scoring, demand forecasting, fraud detection, logistics optimization.
02
RAG system on proprietary data
Connecting an LLM to your internal documentary database to allow your teams to query your data in natural language. Technical documents, knowledge base, contracts, publications.
03
Business AI agent
Design and deployment of an autonomous agent that executes complex workflows: automatic email processing, document analysis, report generation, decision support.
04
Computer vision system
Development and deployment of computer vision models: visual quality control, medical image analysis, object recognition, detection of anomalies on the production line.
Business Cases
They industrialized Data Science and GenAI 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 applied generative AI?
A Data Product is a data or AI solution managed as a business product, with identified users, measured KPIs and a traceable ROI. Diametral manages your data initiatives as sustainable products rather than one-shot projects, which guarantees their adoption and long-term value.
What is an RAG system?
The Data Product Manager acts as a bridge between tech and business teams: he defines the vision of the product, prioritizes the roadmap, measures the value generated and arbitrates changes. Diametral integrates senior Data Product Managers into its squads or trains your own to create this role that is still rare in most groups.
How to measure the ROI of an AI project?
Industrializing an AI agent involves taking it out of demo mode to make it a supervised, measured and governed system. Diametral supervises each agent with safeguards (content filters, human validation on critical actions, complete logs) and measures the value generated monthly to ensure that they remain an asset and not a black box.
What is the difference between POC and industrialized AI?
A POC demonstrates feasibility on a small perimeter, an industrialized AI works in production with SLA, monitoring and integration into business tools. Diametral avoids the eternal POC syndrome by framing the criteria for transition to production from the start: target volume, IS integration, governance and expected ROI.
How to avoid the hallucinations of generative AI?
Avoiding hallucinations is based on four practices: anchoring responses in your documents via a RAG, validating outputs through business rules, setting up human supervision on sensitive cases, and tracking each response for audit. Diametral integrates these layers by default into every generative AI deployment in business.
contact
An AI use case in mind? Let's build it together.
Describe your problem. A Diametral Senior Data Scientist assesses the feasibility and offers you an initial approach.