95% of AI projects fail because they lack focus. For more than 8 years, QALEON has been part of the 5% that succeed. Better insight, faster decisions, lower costs and more time back in your day. And you start seeing it within the first few weeks.
Years of experience
of clients come back with new projects
professionals
countries where we operate
sectors with live projects
Scalable AI technology for real-world environments
Computer vision · Natural language processing · Neural networks and deep learning · Predictive analytics
Applied machine learning · Agentic AI · Agent orchestration · Model fine-tuning · Workflow integration
Data governance · MLOps · Regulatory compliance (GDPR, MiFID, EU AI Act and others)
We analyse customer sentiment with natural language processing, drawing on reviews and mentions across every channel, and raise the alarm before an issue escalates.
40% impact on revenue
AI-driven energy management that combines IoT and SCADA sensor data with predictive models of consumption and demand in real time.
60% energy savings
A corporate assistant that brings SAP, SharePoint, email and other tools together into a single layer of intelligence, with full traceability from end to end.
48% less time spent on administration, referral approvals, billing and test requests
We analyse space weather data and apply innovative models and techniques to forecast extreme weather events more accurately, with greater reliability and effectiveness than conventional meteorological models. Climate · Energy · Insurance
The name is no coincidence. It comes from our chameleon, a symbol of change, evolution and adaptation. Five phases, each one combining technical rigour with close, hands-on support.
Today we research what will be standard practice in every project tomorrow
We do not take an oversized generic model and cut it down to fit afterwards. We design the size and the architecture around the task itself, using pruning, quantisation and knowledge distillation.
We measure what each model actually consumes, not only how well it performs. Energy use per inference, in kWh, is a product metric in its own right, tracked just as closely as accuracy.
We audit for bias using quantifiable fairness metrics, apply explainability frameworks such as SHAP and LIME, and meet the requirements of the EU AI Act ahead of the deadline.
"A 50-80% reduction in energy consumption, with no loss of performance"
Every time a model recommends a price, a candidate, or a maintenance route, executives face an uncomfortable question: follow the suggestion, or trust their own judgment? AI leadership is no longer about deploying tools — it’s about deciding when to delegate judgment and when to withhold it. Teams that treat every algorithmic recommendation as just […]
Security teams rarely fail for lack of alerts. They fail for lack of time. By the time the SOC confirms lateral movement, the attacker has been inside for days and the containment window is down to hours. Predictive cybersecurity flips that sequence: it combines historical data with real-time signals to estimate where the next incident […]
The pilot worked. In six weeks the team had a model with solid results on historical data, a convincing demo and sign-off from the steering committee. Nine months later, that same system absorbs a disproportionate share of the data team’s time and nobody wants to touch it. That gap has a name: AI technical debt. […]
