How many decisions do you make without looking at your own data?
We apply Machine Learning to your business to turn your sales and customer history into concrete decisions — not spreadsheets nobody opens again.
Why businesses trust us
Results with clear goals
We define efficiency and savings goals before automating, and measure that they are met.
Security and GDPR compliance
We work under strict security protocols and European regulatory compliance.
Ongoing support and optimization
We do not disappear after delivery: we support and fine-tune every automation over time.
For any kind of business
Ecommerce, public administrations, agencies and local businesses - no advanced tech setup required.
This is what we do with your data
Four concrete ways to apply Machine Learning to a business like yours.
Customer segmentation
You send the same campaign to every customer, even though they buy completely different things.
We group your customers by real buying habits so each campaign reaches the audience that will actually respond.
- We detect which customers only buy on sale and which buy at full price.
- We identify your most valuable customers so you can treat them differently.
Demand and stock forecasting
You buy stock by gut feeling: sometimes you run out, other times you're stuck liquidating leftovers.
We predict future demand from your sales history, so you buy what you're actually going to sell.
- Anticipate demand spikes during campaigns and peak seasons.
- Reduce the dead stock that never sells.
Bundles and cross-selling
You sell products separately even though many customers always buy the same things together.
We detect which products get bought together to create bundles and recommendations that increase average order value.
- Automatic bundles based on real buying patterns, not guesswork.
- Recommend the right product at the right moment.
Customer churn prediction
You find out a customer has left once they've gone months without buying — by then it's too late.
We detect early warning signs so you can act before losing the customer, not after.
- Automatic alerts when a customer's usual activity drops.
- Prioritize who to contact first based on real churn risk.
This is how we work with you
Requirements gathering
We meet with you to understand your needs.
Assessment of your case
We analyze your situation, define the best solution and send you a proposal.
Development & customization
We build a tailored system and test it against your real needs.
Testing in your environment
We connect the automation to your tools: Gmail, Drive, Slack, your CRM...
Your own testing
Before going live, you try it yourself. We fine-tune it with your feedback.
Delivery
We hand everything over, explain how it works and provide support.
Where do I start?
We offer a FREE initial audit
Before you book, let's clear up your doubts about Machine Learning
The questions companies ask us most before starting a data project.
Do I need a lot of data for this to work?
You don't need a huge volume of data to get started. We first analyze what data you already have (sales, CRM, spreadsheets...) and tell you if it's enough for your specific case. Many projects start with the history you're already generating without realizing it.
What if my data is messy or spread across several places?
That's the most common situation, not a blocker. Part of our job is cleaning and unifying that data before applying any model.
How long does a Machine Learning model take to show results?
It depends on the case, but the first indicative results are usually visible within a few weeks. Models also improve over time as they receive more real data.
Does this replace my team or a data analyst?
No. The model does the heavy lifting of analyzing large volumes of data, but your team still makes the decisions, with better information on the table.
Does this work for any type of business?
It works whenever there's a data history (sales, customers, operations...). The longer you've been generating that data, the better the results, but you don't need to be a large company to get started.
How is this different from Excel or a Business Intelligence dashboard?
A dashboard shows you what has already happened. Machine Learning goes a step further: it learns from that data to anticipate what's going to happen and recommend what to do about it.