Model obfuscation
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.
Evolutionary AI platform
Unique, robust and optimized AI models for your business.
AI that no one can identify or copy, free of the biases you reject, evolving at the pace of your data and consuming less — while your data and key parameters never leave your company.
- Obfuscation : mask the signature and behavior of open-source-based models
- Bias counterfight : eliminate models carrying unwanted biases through selection
- Data drift : models that evolve at the pace of your data
- Optimization : reduce size, latency and consumption, from edge devices to LLMs
- Confidentiality : data and model weights stay inside your company
What we solve
Concrete problems, one neuroevolutionary technology
Our mutation, competition and selection engine does more than produce high-performing models: it answers the major challenges of AI in production.
Bias counterfight
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.
Data drift
Your data evolves, your model does not: its performance silently degrades until an incident occurs, and manual retraining always comes too late.
Optimization and efficiency
Oversized models, LLMs that are costly to fine-tune and serve, consumption and latency incompatible with edge deployment or scaling.
Deliver value for all stakeholders
For leaders
RAISE turns AI model design into an industrial process: fewer manual trials, easier deployment, better-sized models that are hard to replicate.
For AI teams
RAISE augments data scientists: it automates architecture and hyperparameter exploration while leaving them in control of scoring, constraints, frameworks and the final model.
A solution that respects enterprise confidentiality and fits into their workflow
RAISE integrates with your MLOps stack through two flows you define: a model-generation flow and a champion-extraction flow to production. Data stays in the enterprise environment, only the topology is exchanged (never the weights), models can be exported in standard formats and scoring can include production constraints from the design phase.
What RAISE makes possible
Generate an AI tailored to your problem
RAISE explores the architecture space and delivers a model aligned with your data, objectives and production constraints, without moving data outside your environment.
Optimize an AI already in production
For equipped teams, RAISE improves existing model topologies, including from open-source bases, and searches for better trade-offs between score, size, speed, robustness and adaptation to dataset changes.
Vision
Specialized AI instead of generic models
DataValoris is built on a simple conviction: the next step in AI is not only to use large existing models, but to automatically design specialized models that are leaner, more robust and better adapted to each business context.
Ready to industrialize your next AI?
Tell us about your challenges — we may have the solution…