Stop reporting on the past.
Start predicting what's next.

Build and deploy ML models, churn, forecast, scoring,in hours, not months.
No Python. No data scientist.
Just your data analyst asking questions in plain English.

Trusted by data teams across Europe
LE PROBLÈME

Vous constatez. Vous ne prévoyez pas.

01

Là où les idées vont mourir

Le rétroviseur : les rapports disent ce qui s'est passé le mois dernier.

L'intuition : les prévisions tiennent dans un Excel et dans la tête d'une personne.

Le projet IA : un data scientist, six mois, et un modèle qui ne sort jamais du notebook.

FROM QUESTION TO PREDICTION
Ask a question. Get a model in production.
Tell Matr what you want to predict,in plain language.
Matr identifies the right datasets and starts working.
Every step is visible and explainable. No black box.
Matr selects features, tests algorithms, and trains the model.
The model goes live, no setup.
Predictions refresh with your data, via dashboard, API, or direct integration.
Set it once. No MLOps team required.
Matr monitors performance and flags when retraining is needed.
SIX MONTHS VS. ONE WEEK
The real cost of "we'll build it ourselves."
Time to first prediction

La rupture de samedi, annoncée mardi.

Annual cost

From €12K/Year

Data pipelines

Built-in, real time

LE SOCLE
Un modèle entraîné sur des données sales prédit des erreurs.

MatR entraîne ses modèles sur des données nettoyées et réconciliées, avec un historique fiable.

Couche sémantique MatR : une définition par indicateur, utilisée par les tableaux de bord, Slack, l'API et votre IA

Une prévision juste commence par une donnée juste.

THREE WAYS OUT
Use predictions wherever you work.

Once your model is in production, Matr gives you four ways to consume predictions:

Every model ships with a ready-to-use API endpoint. Copy the cURL, test it in 30 seconds.
case study

Blissim cut churn by 20%

without hiring a data scientist

The Challenge

Blissim was losing €200K/month to churn. No data scientist on staff. No time to build a model from scratch.

The Solution

MatR a déployé un modèle de prédiction du churn sur BigQuery en une journée. Le modèle score chaque client, identifie les profils à risque et alimente les campagnes de rétention.

L'impact

  • +20% customer retention
  • +40K/month recovered revenue
  • Model deployed and in production

Questions we hear most.

Every month you spend building ML in-house is a month your competitors spend shipping predictions.

  • Do I need a data scientist to use Matr?

    No. That's the whole point. Your data analyst describes the business question in natural language — Matr handles the ML pipeline.

  • What types of models can I build?

    Classification (churn, scoring, segmentation), regression (revenue forecast, demand planning), and time-series forecasting. Matr selects the best approach based on your data.

  • How accurate are the models?

    It depends on your data quality, but Matr shows you accuracy metrics, baseline comparison, and confidence scores so you can make an informed decision. Typical results: 75-95% accuracy on well-structured data.

  • Where does my data stay?

    In your warehouse. Matr connects to your Snowflake, BigQuery, or PostgreSQL. No data is moved or duplicated.

  • Can I integrate predictions into my existing tools?

    Yes. Via REST API, direct warehouse write-back, or scheduled exports. Predictions flow into your CRM, ERP, spreadsheet — wherever your team works.

  • What happens if the model's performance degrades?

    Matr monitors model drift continuously. When performance drops below threshold, it flags the issue and can trigger automatic retraining.

For more questions, feel free to contact us