Our core beliefs
Building systems that create a powerful synergy between human expertise and machine intelligence
eaQbe service stack
End-to-end consulting: from data foundations to decision intelligence at scale across your organization
Build the data backbone
We build the unified foundation your data needs to scale. By mastering integration, modeling, and governance, we transform scattered sources into a trusted asset—ensuring consistent analytics across teams and tools.
Turn data into decision intelligence
We design and optimize end-to-end BI systems, from data modeling and KPI definitions to dashboards, visual storytelling, automated reporting, and alerts. Built for adoption, consistency, and decision velocity across your organization.
Custom Algorithmic Solutions
We engineer tailored algorithms to solve specific operational challenges. When off-the-shelf analytics isn’t enough, we build custom logic, from optimization to advanced pattern recognition. Transparent by design, our “white-box” systems keep decision-making explainable and auditable.
Our available expert consultants
Strengthen your team with senior, immediately operational profiles - for short or long-term engagements, depending on your needs
Senior Consultant Qlik Sense
➜ Advanced Data Visualization
➜ Sophisticated Data Modelling
➜ QMC Administration & Governance
➜ Big Data performance Optimization
Senior Consultant
Power BI Fabric
➜ Power BI Development, DAX & Power M
➜ Microsoft Fabric Dataflow & Pipeline Design
➜ Microsoft Fabric Administration & governance
Senior Consultant
Machine Learning
➜ Features engineering
➜ ML Model Development & Training
➜ Agentic Workflows
Our methodology
A structured consulting approach grounded in CRISP-DM and delivered through Scrum
1. Frame the challenge
We start by structuring the project at business level: stakeholder alignment, governance setup, and qualitative interviews across departments. Through steering committees and structured discussions, we clarify objectives, success criteria, constraints, and interdependencies. In parallel, we assess available data sources to ground decisions in operational reality.
2. Build and iterate
We translate business objectives into data pipelines and models. This phase covers features engineering, and iterative modeling, with frequent validation points. Working in short cycles, we test assumptions, refine configurations, and ensure models remain explainable, robust, and aligned with business intent.
3. Validate and operationalize
We evaluate results against the initial business objectives. Once validated, we deploy solutions into existing systems, support user adoption, and document outcomes. This phase closes the loop with stakeholders and sets the foundation for continuous improvement.
