AI is moving into physical systems. The intelligence is ready. The authority is not.
Xpdeep designs natively explainable AI models that enable machines to make defined decisions under explicit operating conditions.
ECONOMIC VALUE. STRATEGIC ADVANTAGE.
The next AI shift
Deep learning transformed perception.
LLMs transformed software intelligence.
Orchestrators are turning intelligence into coordinated action.
In robotics and autonomous machines, this shift is increasingly described as Physical AI.
The same movement is reaching vehicles, industrial equipment, energy systems, medical devices and mission-critical infrastructure.
From digital intelligence to physical intelligence — A historical progression
DEEP LEARNING
Transformed perception — and made the LLM revolution possible.
FOUNDATION MODELS / LLMs
Brought broad intelligence into software.
AGENTS & ORCHESTRATION
Intelligence coordinates tools, models and actions.
PHYSICAL AI
Learned intelligence perceives, reasons and acts in the physical world.
The industrial deadlock
This problem extends beyond robotics.
Any consequential learned system can face the same limit when the model cannot state enough about its own decision.
Deep learning is powerful enough to model complex physical systems.
But when the learned model cannot state:
Human closure
Experts review decisions the model cannot carry alone.
Conservative limits
Useful performance remains unused because uncertainty cannot be bounded.
Redundancy
Additional rules, checks and sensing compensate for model opacity.
Blocked product functions
Functions that require greater delegated authority remain difficult to deploy.
The result is simple:
higher operating cost, unused performance, and product functions that never reach the market.
The system is not limited only by model accuracy.
It is limited by how many consequential decisions the model can take without handing them back to a human.
The model
So that is where Xpdeep changes it.
Today, black-box models are bounded from the outside through conservative operating limits, monitoring and fallback rules.
Xpdeep changes the model itself.
Conventional deep model
INPUTMODELPREDICTION
Questions remain outside the model:
Xpdeep model
INPUTMODELBOUNDED DECISION OBJECT
Xpdeep changes what the learned model itself returns.
Not the customer’s engineering stack.
This is a model-level capability, not an external governance layer.
See how the model architecture works →One model. Three loops.
01 — DESIGN
Correct the model and align it with the outcomes that matter.
Target accuracy where it matters, reduce unnecessary inputs, simplify the model, and optimize memory or inference footprint without rebuilding everything from scratch.
After initial training, engineers can read the learned decision structure, diagnose a problematic region and apply a targeted local correction while freezing the rest of the model.
Resulting shift
Trial-and-error trainingModel engineering
Read → diagnose → modify → verify
02 — PRODUCTION
Move routine decisions from human closure to bounded model authority.
The model predicts, explains, checks its domain state and, where appropriate, computes a minimal feasible intervention under constraints with structural justification.
Resulting shift
PredictionBounded decision input
The surrounding system stays in control.
03 — CONTROLLED EVOLUTION
Limit change to the affected model region, then validate what changed.
Targeted correction can create the conditions for a narrower revalidation scope.
Operational evidence can be used to identify and correct a specific model region between operating periods, while preserving unchanged behaviour elsewhere.
Resulting shift
RebuildTargeted correction
Controlled evolution is validated change between operating periods. It is not continuous learning.
Adoption
Retrofit what you already have.
Go native when you want the full three loops.
Retrofit
Keep your existing PyTorch predictive model frozen.
Add:
No targeted structural editing of the frozen predictive model.
Native
Use the full Xpdeep architecture for:
No change to your existing MLOps stack.
Same engineering lifecycle. A different way to engineer the model inside it.
Built to contribute model-level evidence to existing qualification and assurance processes. Xpdeep does not replace the customer’s safety, validation or certification framework.
The economic consequence
When model authority becomes explicit, more of the machine’s capability becomes usable.
More machines per expert
Expert attention moves toward true exceptions rather than routine model closure.
More usable performance
Capability currently held back by conservative uncertainty margins can potentially become usable inside characterized conditions.
New product functions
Functions become feasible when the learned model can carry a defined part of the decision instead of handing it back systematically.
New operating models
Products, processes and service models can be redesigned around bounded delegated intelligence.
Bounded authority is the enabling mechanism.
Industrial transformation is the destination.
Where learned decisions become physical
How much of the decision can the learned model carry before the system must hold or escalate?
Vehicle-state intelligence can support more machine-level decisions when domain validity remains explicit.
WHEEL SPEED
STEERING
VEHICLE STATE
LEARNED MODEL → DECISION
ACT · HOLD · ESCALATE
Evidence
Selected validated results across Confiance.AI and client programmes.
Less complexity to instrument and maintain
105 → 19 Input variables
Up to 82% fewer at equal or better accuracy.
Selected validated results across Confiance.AI and client programmes.
Less engineering iteration
Up to 4× Faster model development
Selected validated results across Confiance.AI and client programmes.
More deployment headroom on constrained targets
Up to 35× Faster inference
Independent programme evaluation.
Industrial programmes beyond the lab
12+ Enterprises
On the Xpdeep platform.
Company figure.
Machines live on state, signals and time.
Vehicles, industrial equipment, medical devices and mission-critical systems continuously express their physical state through temporal signals.
This is where Xpdeep has its deepest scientific edge.
01
Physical system
02
Live state
03
Temporal signals
04
Learned model
05
Bounded decision
ACT · HOLD · ESCALATE
Where Xpdeep is strongest today
Time series & signals
Deep structural insight into machine state, temporal dependencies and decision dynamics.
Vision
Structural explanation, model and dataset diagnosis, attributed domain awareness and novel-class detection on learned perception models.
Xpdeep has also demonstrated dataset optimization, unknown-class detection and significant accuracy gains across vision and robotics programmes.
image-level prescription: in development
Xpdeep complements Physical AI architectures where the consequential decision is carried by learned models over signals, state, temporal data or downstream learned representations.
BUILT IN EUROPE
Built in Europe for organizations that need to retain control of critical learned systems.
Xpdeep is developed in France from research originating at LIG / CNRS / Université Grenoble Alpes.
Start small
A large vision does not require a large first commitment.
Prove the economics on one decision.
Expand only where the evidence supports it.
One decision
A decision your business already owns.
One measured KPI
Performance agreed before the engagement starts.
Evidence
Measure what changed in the model, the decision and the business outcome.
Expansion
Scale to more decisions, assets and products only where results support it.
Machines Will Decide.
We Decide How.
Physical AI brings learned intelligence into the physical world.
Xpdeep makes the authority of learned models explicit and bounded.
The organization defines the conditions.
