QALEON Lab: where we research the AI we later take into production
Green algorithms
We design and select algorithms and architectures explicitly to minimise computational and energy consumption, without sacrificing performance. We do not start with an oversized generic model and trim it afterwards: the size and the architecture are worked out at the design stage, around the real task to be solved.
Model pruning
We remove connections and parameters that contribute very little to the result, reducing model size and the cost of inference.
Quantum-like
We reduce the numerical precision of the model's weights (from 32 bits to 8 bits, for example) to speed up inference and cut memory use, without compromising performance.
Knowledge distillation
We train smaller "student" models to reproduce the behaviour of larger "teacher" models, keeping most of their capability at a fraction of the size.
Architecture matched to the task
Rather than reaching automatically for the largest model available, we apply the architecture best suited to each use case. A small, specialised model often outperforms a large, oversized generic one.
Energy efficiency of algorithms
We measure and optimise the real energy consumption of every model, not only its predictive performance. Energy efficiency is treated as one more product metric, tracked as closely as accuracy or latency.
Measuring consumption per inference
We quantify the energy used (in kWh) per prediction or per task, so that alternatives can be compared objectively before deciding which model goes into production.
Choosing efficient infrastructure
We select the hardware and the cloud or on-premise infrastructure best suited to each workload, avoiding oversized compute resources.
Optimising training cycles
We cut out unnecessary retraining and match how often models are updated to the real pace of change in the data, rather than to an arbitrary calendar.
Carbon footprint reporting by project
We quantify the environmental impact of every solution deployed, and that figure feeds directly into the client's ESG performance.
This is the work behind our promise of efficient, sustainable AI by design: a reduction of between 50% and 80% in energy consumption compared with conventional AI solutions.
Applied AI ethics
Research into AI ethics at QALEON Lab does not stay theoretical. It turns into concrete methodologies and tools that are then applied on every client project, reinforcing the responsible, auditable AI approach already set out under “What we do”.
Our own bias auditing methodologies
We develop and refine fairness metrics to assess models before and after they go into production.
Explainability frameworks
We adapt explainability techniques (such as SHAP and LIME) to each type of model and use case, so that AI decisions can be justified to a regulator, a client or an end user.
Research into EU AI Act compliance
We work on risk classification and technical documentation methodologies that anticipate regulatory requirements, rather than reacting to them once they are already mandatory.
Human oversight patterns by design
We research and document the most effective human-in-the-loop checkpoints for each type of decision and level of criticality.
QALEON Lab is not a department off to one side: it is the research engine that makes sure every client project includes the latest thinking in AI efficiency and ethics, not only what has already been proven.
