Product Delivery
Track exactly how much your AI generates every month, in euros.
We run your Data and AI initiatives as business products: with users, KPIs and traceable ROI. Not as tech projects that vanish once delivered.
+150 companies supported
— 5/5 on Google
Benefits / Impact
What it delivers
Concrete results for your organization, measurable from the very first weeks of our engagement.
01
Visible ROI you can defend before the executive committee
Every Data or AI product is tied to business value indicators. You can show precisely what your AI investments return, month after month.
02
AI products adopted because they serve the business
A Data product managed like a real product is designed with its users, not for them. Adoption is no longer an issue, because the business need is at the center from day one.
03
The end of one-shot AI projects
The product approach turns a one-off project into a lasting asset. The product evolves, improves and generates value over time instead of being delivered and forgotten.
04
Informed budget decisions
When every AI product has a measured operating cost and an ROI, investment decisions become rational. You know where to invest more and where to cut.
05
A shared language between tech and business
The Data Product Manager bridges technical teams and business leadership. No more approximate translation: objectives are shared, priorities aligned.
Chapters
Our expertise
The skills and know-how we deploy to deliver results in this area of expertise.
Tools / Partnerships
How we put this expertise into practice
Data Product Management is not about tools but about method. We use product management and Lean standards, adapted to the specifics of Data and AI products.
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
Data Product scoping
Defining the scope, target users, value KPIs and product backlog. Delivered in 4 to 6 weeks with an identified business sponsor and a scoped MVP.
02
Data Product management
Establishing the Data Product Manager role on an existing product or one under construction: roadmap, value measurement, team rituals and sponsor reporting.
03
Data Product Management program
Deploying a Data Product Management team across a business unit or group: training, tooling, rituals and portfolio governance.
04
AI portfolio value measurement
Audit of an existing portfolio of Data and AI initiatives, implementation of a value measurement framework and decisions on which products to continue, optimize or stop.
Business Cases
They managed their Data & AI products with us
We don't deliver POCs. We deliver systems that work, with 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 a Data Product?
A Data Product is a data or AI solution managed as a business product, with identified users, measured KPIs and traceable ROI. Diametral runs your data initiatives as lasting products rather than one-shot projects, which ensures their adoption and long-term value.
What does a Data Product Manager do?
The Data Product Manager bridges tech and business teams: they define the product vision, prioritize the roadmap, measure the value generated and decide on changes. Diametral embeds senior Data Product Managers in its squads or trains yours to create this role, still rare in most large organizations.
How do you measure the ROI of an AI project?
Measuring the ROI of an AI project relies on three families of indicators: business value (revenue generated, avoided costs, productivity), adoption (active users, frequency of use) and technical quality (SLA, drift). Diametral sets up these indicators from the scoping phase and reports them monthly to the sponsor.
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 into business tools. Diametral avoids the never-ending POC syndrome by defining the criteria for going into production from the outset: target volume, IT system integration, governance and expected ROI.
How do you manage a portfolio of AI products?
Managing a portfolio of AI products relies on clear governance, regular measurement of each product's value and explicit decisions (continue, optimize, stop). Diametral puts in place the framework, indicators and committees that turn a pile of POCs into a managed portfolio.
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 propose an initial approach.