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"
An AI model doesn’t fail at random: it fails because it learned from data that was already distorted, and that distortion went unnoticed during training. Data bias doesn’t announce itself with an explicit alert: it builds up gradually, through incomplete records, poorly defined categories, or past human decisions the algorithm interprets as the norm. For […]
A video call with your CFO can be fake, and nobody in the room would know while it’s happening. Corporate deepfake fraud has moved past novelty status into a real attack vector that combines cloned voices, synthetic video and time pressure to push through wire transfers, approve contracts or extract confidential data. The Arup case […]
When an AI model declines a loan, ranks a job candidate, or flags an anomaly on a production line, the question that follows is almost always the same: why? Explainable AI (XAI) answers that question before it turns into a legal, operational, or trust problem. For operations, technology, and sustainability leaders, understanding the reasoning behind […]
