The data exists.
It just doesn’t reach the decision.
The warehouse is full.
The dashboards are live.
The models are running. And yet most of it stays where nobody looks.
Business teams export to spreadsheets and paste numbers into slides.
Analysts answer the same five questions for the third time. Predictions sit in a table nobody opens.
The cost was never in producing the data, it was in the distance between the data and the decision.
Closing that distance is what data consumption is about.
Use predictions wherever you work.
Once your model is in production, Matr gives you four ways to consume predictions:
The real cost of"we'll build it ourselves."
"Every month you spend building ML in-house is a month your competitors spend shipping predictions."
< 1 Week
From €12K/Year
Built-in, real time
Automatic monitoring
Yes - Natural language
One click
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.
