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Data you understand, decisions you can anticipate.

Analytics connected to your operations and predictive models trained on your historical information. To understand what's actually happening, anticipate what's coming and back decisions with real criteria.

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The Problem

You have data everywhere and no clear view

  • You decide by intuition, not by data

    Each decision depends on the judgment of whoever makes it because no one has the cross-data in time. Small misaligned decisions that, accumulated, cost thousands of euros.

  • Your data lives in disconnected silos

    Each system holds part of the truth. No one has a global view of the business or can answer a simple question without cross-checking sheets and tools.

  • You react late to problems

    You detect a client leaving, an operational bottleneck or an anomaly when it's already too late to act.

  • Reports consume days of the team

    Month-end close, quarterly report or a specific client analysis takes whole days cross-checking spreadsheets and disparate systems.

The Solution

We turn your data into visibility and foresight

We analyze the areas that most condition business performance. The questions and patterns are similar across sectors: sales, operations, costs, customers.

Sales and revenue

Revenue evolution over time. Performance by product, channel or area. Purchase patterns and future demand forecasting.

Operations and processes

Bottlenecks, delays and operational efficiency. Resource use and capacity. Anomaly detection before they affect service.

Costs and performance

Cost structure, margins and real profitability. Identification of cost drivers and economic efficiency opportunities.

Customers and demand

Customer behavior and segmentation. Retention and churn indicators. Churn prediction and next best action.

Custom predictive models

We build specific models on your real history: demand forecasting, customer scoring, fraud detection or predictive maintenance. Grounded predictions, not generic templates.

Alerts and reports that come on their own

The system warns when it detects an anomaly or a critical pattern. Reports are generated and sent to the right person, without manual intervention.

Results

What you get with predictive analytics

1 source

A single source of truth for the whole team

Foresight

You know what's coming before it happens

Real

Your data and your decisions, without lag

<6 wks

Average time of first model in production

Cross-sector cases

What is already being applied

4 cross-sector cases

  • Demand and sales forecasting

    Models that anticipate sales by product, channel or period. Purchasing, production or commercial teams plan with data, not intuition.

  • Early churn detection

    Identification of clients at risk of canceling or reducing activity before it happens. The team intervenes in time and improves retention.

  • Scoring and prioritization

    Each lead, client or order receives a score with closing probability, value or risk. The team prioritizes with shared criteria.

  • Anomaly detection and alerts

    The system detects patterns out of the ordinary in sales, operations, quality or payments. It warns the responsible person before the problem is visible to everyone.

FAQ

Questions about predictive analytics

4 frequently asked questions

Depends on the case. For sales forecasting or churn detection, 12-24 months of history is usually enough. For anomaly detection or predictive maintenance, it depends on event frequency. In the diagnosis we evaluate what you have and what's missing.

For visualization: Power BI, Metabase or Looker Studio on top of the tools you already use. For modeling: Python or R depending on the case. For integration: native connectors to any ERP, CRM, ecommerce or database with API. The specific choice is made after the diagnosis.

Almost always. We build the unification pipeline as part of the project, so the rest of the team works on a single source of truth. Without touching the operational tools where data is generated.

We implement automatic monitoring that detects model drift. If you need ongoing maintenance, we manage it. If you prefer to handle it in-house, we transfer the knowledge to the team.