Applied AI and Data Science
Orchestrate your AI agents to maximize their impact without losing control.
Everyone is experimenting with GenAI. Few companies know how to scale it effectively. We design, develop and orchestrate your Data Science models and AI agents so they deliver reliable results in production — not just impressive demos that fall apart in real-world conditions.
+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
AI models that solve business problems
Every model we develop serves a defined business objective: reducing a cost, speeding up a process, improving a prediction. No modeling for modeling's sake.
02
Orchestrated, controlled AI agents
Your GenAI agents don't run unchecked. We put in place the guardrails, monitoring and validation workflows that ensure reliable, controlled responses.
03
From POC to production, included
We don't deliver a notebook. We deliver an industrialized system, integrated with your business tools, deployed in production and monitored. The POC is just a step, not the deliverable.
04
Putting your proprietary data to work
The real value of GenAI in business emerges when it works on your data, not on generic knowledge. We connect LLMs to your information assets for results specific to your context.
05
Upskilling your teams
Our Data Scientists and AI Engineers work alongside yours, not in their place. Knowledge transfer is built in so your team becomes self-sufficient with the new techniques.
Chapters
Our expertise
The skills and know-how we deploy to produce results in this area of expertise.
Tools / Partnerships
How we put this expertise into practice
We work with all the leading frameworks and platforms on the market. The technology choice is driven 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
Project types
What we deliver
Operational deliverables, not recommendations. Here are the engagement formats we deploy in this area of expertise.
01
Industrialized predictive model
From scoping 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 document base so your teams can 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: automated 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, anomaly detection on production lines.
Business Cases
They industrialized Data Science and GenAI 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 applied GenAI?
Applied GenAI refers to industrializing models such as GPT, Claude or Mistral on a company's proprietary data to automate business tasks. Diametral deploys custom RAG systems, autonomous agents and copilots, with human oversight, governance and measurement of the business value generated.
What is a RAG system?
A Retrieval-Augmented Generation (RAG) system combines a language model with a proprietary knowledge base to generate answers grounded in your documents. Diametral deploys RAG on contracts, procedures, product catalogs or support knowledge bases, eliminating the hallucinations typical of general-purpose LLMs and ensuring source traceability.
How do you industrialize an AI agent?
Industrializing an AI agent means taking it out of demo mode and turning it into a supervised, measured and governed system. Diametral frames each agent with guardrails (content filters, human validation of critical actions, complete logs) and measures the value it generates month after month to make sure it remains an asset rather than a black box.
What is the difference between a POC and industrialized AI?
A POC demonstrates feasibility on a small scope; industrialized AI runs in production with SLAs, monitoring and integration with business tools. Diametral avoids the endless-POC syndrome by defining go-to-production criteria from the start: target volume, IT integration, governance and expected ROI.
How can you prevent GenAI hallucinations?
Preventing hallucinations relies on four practices: grounding answers in your documents through RAG, validating outputs against business rules, putting human oversight in place for sensitive cases and logging every answer for audit. Diametral builds these layers in by default in every enterprise GenAI deployment.
contact
Have an AI use case in mind? Let's build it together.
Describe your problem. A Diametral Senior Data Scientist will assess its feasibility and suggest an initial approach.