Automatically generate and optimize your AI

Model obfuscation

The problem

An open-source model deployed as-is is identifiable: its topology and behavior form a recognizable signature that adversaries can exploit — targeted attacks, reverse engineering, prediction of its reactions.

Our answer

Mutations produce a topology that is no longer the original one, and selection keeps mutants that do not make the same errors as the initial model: the structural and behavioral signature disappears. Ensembling mutants blurs the behavior even further, and formal-validation tools (such as Numalis) make it possible to keep only fully validated models. Available as SaaS — which only ever sees the topology, never the weights or the data — or as an on-premise offer for the most sensitive environments.

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Bias counterfight

The problem

Unwanted biases (demographic, historical, sampling) silently settle into models and create regulatory risk — reinforced by the European AI Act — as well as ethical and commercial risk.

Our answer

Your anti-bias criteria are built into the selection score: models carrying the biases you want to avoid are eliminated from the competition, generation after generation. Ensembling models with different biases also enables consensus-based decisions.

Data drift

The problem

Your data evolves, your model does not: its performance silently degrades until an incident occurs, and manual retraining always comes too late.

Our answer

The evolutionary mechanism mutates the model at the pace of your data: with every dataset variation, competition re-selects the architectures best adapted to the new context, continuously.

Optimization and efficiency

The problem

Oversized models, LLMs that are costly to fine-tune and serve, consumption and latency incompatible with edge deployment or scaling.

Our answer

Multi-objective selection (score, parameter count, speed, consumption) produces smaller, leaner models at equal performance: mini-LLMs, distillation, edge models. For LLM fine-tuning, RAISE optimizes the adaptation layers added to the foundation model — the base model itself stays frozen.