What problems can a predictive model solve?

A model can anticipate demand, sales, risks, failures or customer behaviour, among other things. It can also produce scores, recommendations or probabilities in order to improve a business decision.

What is the difference between prediction and optimisation?

Prediction seeks to estimate what is likely to happen. Optimisation then uses the available data and constraints to determine the best possible decision: scheduling, inventory, resource allocation, pricing or routing, for example.

How much data is needed to build a predictive model?

There is no universal volume. It depends on how frequent the events are, the number of variables, seasonality and the level of accuracy sought. We first analyse the quality and representativeness of the data before choosing an approach.

Does our data have to be perfectly clean before we start?

No. We can take care of its extraction, cleaning, preparation and analysis. A model cannot, however, compensate indefinitely for insufficient or unrepresentative data; that limit has to be identified from the outset.

What accuracy can be expected from a model?

It depends on the problem and on the data available. We measure performance on data that the model did not use for its training, and we define with you a threshold that makes it possible to decide whether the result is good enough to be used.

What happens when the model gets it wrong?

The answer depends on the criticality of the use case. The model can produce a recommendation that is left to a user for validation, trigger an alert, or automate a decision only above a certain level of confidence.

Can the model be tested on our historical data before the production rollout?

Yes. We can replay the predictions over historical periods in order to compare what the model would have proposed with the results actually observed. This is an essential step for measuring its value before integration.

How are predictions integrated into our existing tools?

Models can be exposed through an API or integrated directly into an application, a dashboard, an ERP, a CRM or a workflow. The aim is for the prediction to appear at the right point in the decision process.

How much does a predictive project cost and how long does it take?

That depends notably on the state of the data, the number of models, the level of accuracy expected and the integrations. We can separate a phase for validating the potential and then, if the results are conclusive, an industrialisation phase.

How is the ROI of a predictive or optimisation model measured?

We tie it to the decision that has been improved: a reduction in inventory, better allocation of resources, fewer breakdowns, higher margin, less fraud or improved forecasting. The gain has to be compared with the cost of developing and running the model.

[ Experts in predictive AI & optimisation ]

Anticipate. Decide. Optimise.

We develop predictive models and optimisation algorithms that turn your historical data into operational decisions.

Forecasting demand, recommending the best action, optimising a schedule or detecting an anomaly: we build models tailored to your business constraints.

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All our Data & AI expertise

ThinkBusiness objective: defining the decision to improve and the expected ROI.

BuildData analysis, modelling, validation, integration.

OperateMonitoring: tracking performance and retraining as the data evolves.

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[ They trust us ]

More than 100 French and European companies trust us

[ Expertise ]

Turn data into decisions.

Forecasting & time series

We build forecasting models that anticipate sales, demand, stock levels, consumption, footfall or load.

Forecasts come with confidence indicators and can be recalculated continuously.

Scoring & recommendation

We develop systems that estimate a probability, rank an opportunity or recommend the most relevant action, content or product.

Operations research & industrial optimisation

We combine mathematics, algorithms and data to optimise complex systems.

Production, logistics, resources, inventory, routing or allocation can be optimised under several simultaneous constraints.

Scheduling & constrained optimisation

We build engines that automatically generate schedules or allocations respecting availability, capacity, business rules and performance targets.

Predictive models

Classification, regression and machine learning make it possible to estimate future events from your historical data.

We select models based on the accuracy required, but also on their explainability and their ability to run in production.

Anomaly & fraud detection

Our models identify unusual behaviour and deviations that are hard to detect manually.

Fraud, errors, incidents, atypical behaviour or quality issues can be flagged automatically.

Pricing & dynamic optimisation

We develop systems that help determine the right price based on demand, stock levels, seasonality, competition or your business objectives.

Predictive maintenance

We draw on usage, sensor or maintenance data to anticipate failures and intervene before equipment comes to a halt.

Segmentation & classification

We automatically organise your populations, products, documents or events into meaningful groups to make analysis and decision-making easier.

[ USE CASES ]

Concrete applications across your teams.

What our clients hand over to these solutions, team by team, from customer requests to management reporting.

[ ROI ]

AI designed to create value.

Before building anything, we define with you the indicators that will measure the value created:

Time saved
Cost reduction
Volume processed
Error rate
Conversion
Availability
Revenue generated

We prioritise use cases whose impact can be objectively measured.

[ Method ]

Models designed to create value.

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[ SECURITY & SOVEREIGNTY ]

Your data stays under your control.

Public cloud, private environment or dedicated infrastructure: we design the architecture to meet your confidentiality, security and sovereignty requirements.

SHARED
Public cloud
Quick to deploy, pay-as-you-go pricing, European regions — and your data never feeds a third-party model.
ISOLATED
Private environment
Your own dedicated space at the hosting provider: separate network, keys and logs, shared operations.
SOVEREIGN
Dedicated infrastructure
Models hosted on your own servers or in a sovereign data centre: nothing leaves your perimeter.

Access rights, traceability, encryption, anonymisation, guardrails, human oversight and compliance are built in from the design stage.

[ The agency ]

Entrust your project to our experts in AI, data and applications.

Our multidisciplinary team of designers, developers and coaches brings real added value to your solution in the short, medium and long term, thanks to a perfect command of its overall architecture.

30: coaches, developers and solution designers, experts in their fields
200: applications and software products built in-house, tailor-made
98%: happy clients, loyal and ready to continue their project
300: algorithms and code modules, highly functional and 100% adaptable

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[ Tech Stack ]

The best prediction and optimisation technologies.

We select the architecture based on your performance, cost, privacy and integration needs.

The method follows the problem: we start from the accuracy expected, from how far each result has to be explained and from the computing time you can allow before deciding.

We favour approaches that stay readable, so your teams can audit and retrain them on their own once the project is delivered.