AI & Automation · Data Analysis

Transform your data into strategic decisions

Predictive AI, advanced analytics, and custom dashboards: leverage the full potential of your data to anticipate, optimize, and make decisions faster than your competitors.

What is an AI data analysis solution ?

An AI data analysis solution combines your business data (CRM, ERP, web, IoT, sensors…), advanced analytics techniques (BI, data visualization, segmentation), and machine learning algorithms to predict, classify, and recommend.

The goal: move from descriptive reporting (“what happened?”) to predictive and prescriptive reporting (“what will happen? what should be done?”). In practical terms, your teams get access to dashboards that anticipate stock shortages, detect customers at risk of churn, or predict next quarter’s sales with a measurable confidence level.

When do you need a data analysis solution ?

Your data is scattered across multiple tools without a consolidated view.

Your decisions still rely heavily on intuition or Excel.

You want to anticipate demand, churn, or anomalies.

Your teams spend more time producing reports than exploiting them.

You want to automate repetitive decisions (scoring, pricing, alerting).

You want to leverage your IoT, sensor, or log data in real-time.

Use Cases

What AI data analysis can do for you

Sales & Demand Forecasting

Anticipate your sales by product, channel, and geographical area. Optimize your stock, purchases, and production planning with higher accuracy than traditional methods.

Scoring & segmentation

Lead scoring, behavioral segmentation, and churn detection. Identify at-risk customers and prioritize your marketing efforts where the ROI is highest.

Anomaly Detection & Maintenance

Spot fraud, quality drifts, or machine failures before they happen. AI analyzes millions of IoT or transactional events in real-time to reduce your costs.

Custom Decision Dashboards

Real-time dashboards, business KPIs, smart alerts. A single source of truth for your executive committees, operational teams, and customers.

Our Method

From your data to production in 4 steps

Scoping & Data Audit

We identify the priority use case, map your data sources, and assess their quality and availability. Deliverable: a costed specification and an estimated ROI.

1 à 2 weeks

Analytical POC

Development of a prototype on a limited scope : data preparation, initial model, demonstration dashboard. We validate the performance before scaling up.

3 à 6 weeks

Industrialization

Production deployment of data pipelines, model deployment, integration with business tools (CRM, ERP), monitoring, MLOps, user training.

6 à 12 weeks

Run & Continuous Improvement

Tracking model performance (drift, accuracy), adding new features, and new use cases. The AI improves as your data grows.

Continuous

Levels

The right level of analysis according to your data maturity

We adapt the technical complexity and functional scope to your actual needs, not the other way around.

Analytics & Decision Dashboards

Consolidation of your data into a single warehouse, real-time dashboards, business KPIs, automated exports. Ideal for moving from Excel to a modern BI setup.

Targeted Predictive AI

Machine learning models on a priority use case : sales forecasting, churn detection, lead scoring, anomalies. Integrated into your business tools.

Industrialized Data & ML Platform

Data lake, real-time pipelines, multiple ML models in production, MLOps, governance, and monitoring. For organizations scaling data industrialization.

Concrete Case

How we deployed a predictive engine in 10 weeks

Context

Specialized retail player, 180 points of sale, €4M in dead stock. Demand forecasts were made in Excel by product managers, with an error rate of over 35%.

Solution

Unified data pipeline (sales, weather, calendar, promos), hybrid forecasting model (XGBoost + Prophet), management dashboard by category and point of sale, automatic restocking alerts.

Security & Compliance

European hosting, customer data anonymization, auditable logs, GDPR compliance, fine-grained access management by role and business scope.

Adoption

Training for product managers and regional directors, integration with existing tools (ERP, BI), monthly feedback loop to refine models based on field experience.

Résults

− 42%

dead stock in 6 months

+ 11 pts

in forecast accuracy

15 min

to produce reports (instead of 8h)

x 4

ROI over 12 months

FAQ

Questions about AI Data Analysis

What is the difference between traditional BI and predictive AI ?

Traditional BI answers the question “what happened?” through descriptive dashboards (monthly sales, revenue by region, etc.). Predictive AI goes further: it learns patterns in your historical data to anticipate what will happen (next month’s sales, customers who will leave, equipment that will fail). Both are complementary: we often start by modernizing BI, then add predictive layers to the highest ROI use cases.

Do I need a lot of data to get started ?

Not necessarily. Quality over quantity. For a predictive use case, 12 to 24 months of clean historical data is often enough. For BI or data visualization, we can start immediately with your current data. However, unstructured, incomplete, or scattered data is a real roadblock: that’s exactly why we always start with a data audit before making any promises.

Does my data remain confidential ?

Yes. We prioritize sovereign European hosting (OVH, Scaleway, Azure EU, AWS Paris) and guarantee that no customer data leaves your perimeter. For AI use cases, we use your data solely to train your models, never for shared models. The architecture systematically includes anonymization, auditable logs, and GDPR compliance.

What tools do you use ?

We choose the stack based on your existing constraints: Python (Pandas, scikit-learn, XGBoost, PyTorch) for data science; Airflow, dbt, or Dagster for pipelines; Snowflake, BigQuery, or PostgreSQL for storage; Metabase, Superset, Power BI, or Tableau for visualization; MLflow or Weights & Biases for MLOps. We always prefer open-source or portable solutions to avoid vendor lock-in.

How do you measure ROI ?

From the scoping phase, we define clear and measurable business KPIs with you: forecast error rate, value of dead stock, customer retention rate, report production time, etc. These metrics are tracked in the delivered dashboard and allow us to quantify the real impact of the project, month after month.