INDUSTRIES
What changes is the decision, the data and the economics. What stays the same is the need for a model that can carry more of the decision.
ONE ARCHITECTURE
DECISION BOUNDARY
SIX INDUSTRIAL EXPRESSIONS
SELECT AN INDUSTRY
01
Uncertainty in learned state estimates can hold back vehicle functions and add cost through sensing, embedded resources and conservative design margins.
02
03
Structural integration requires read access to the PyTorch architecture and weights. Supplier-owned models require the supplier’s cooperation.
Application areas: virtual sensing, thermal intelligence and perception-domain gating.
04
Build vehicle functions around explainable state estimates, evaluate design margins held for uncertainty, and reduce sensing and embedded-resource requirements where engineering evidence supports the change.
BATTERY · THERMAL · MOTOR TRACE
ILLUSTRATIVE
Actions follow customer-defined system rules. Domain state alone does not authorize action.
CAPABILITIES AND EVIDENCE
Virtual sensors delivered on client programmes and validated by engineering. Structural analysis has also demonstrated impact on ADAS model design.
OUTCOMES TO ESTABLISH ON YOUR APPLICATION
Product functions, material cost, sensing requirements and deployed footprint. Lower engineering effort across product variants remains an application-specific hypothesis.
DATA MODALITIES
DEPLOYMENT ENVIRONMENTS
05 · FIRST ENGAGEMENT
One funded vehicle or embedded decision already owned by the OEM or Tier-1.
Explore Automotive →Vehicle / component state decision
Signals + perception
Design margin / sensing / embedded resources
Mission / platform decision
Signals + perception
Expert attention / mission decision tempo
Process adjustment
Time series
Material / energy / yield / quality
Asset-state decision
Time series
Asset availability / usable performance
Patient / device decision
Time series + tabular
Explained in-session device adjustment
Process / defect decision
Signals + vision
Deviation investigation / yield decisions
Xpdeep’s deepest scientific edge is multivariate time series and digitized signals.
MODALITY
TIME SERIES & SIGNALS
Full production decision object. Native also enables targeted editing and controlled evolution.
TABULAR
Full production decision object. On pure static tabular data, the structural advantage is more limited.
VISION
Structural explanation, attributed domain awareness and novel-class detection. Structural diagnosis of perception models; targeted correction in Native mode.
Prediction and structural explanation share one forward pass. Prescription is a separate inference, invoked when useful on supported temporal and tabular stages.
DEPLOYMENT & CONTEXT
DEPLOYMENT ENVIRONMENT, NOT A MODALITY
Cloud, on-prem or embedded. Embedded describes where a model runs, not the kind of data it reads.
MISSION-CRITICAL
Used where model decisions matter enough that “probably right” is not enough.
Each figure carries its own source. These are model-development and inference figures, not operating-cost, certification or customer-revenue results.
12+
enterprises on the Xpdeep platform
Company figure
105 → 19
up to 82% fewer input variables at equal or better accuracy
Selected validated results across Confiance.AI and client programmes
Up to 4×
faster model development
Selected validated results across Confiance.AI and client programmes
Up to 35×
faster inference
Independent programme evaluation
On time-series data, Xpdeep models achieve accuracy at least equivalent to standard deep learning and frequently superior.
Figures come from different programmes and are not attributed to named customers.
See all evidence → TechnologyRun an Xpdeep model alongside your existing model for an agreed number of weeks. Compare performance on the same data against agreed KPIs, using your own validation chain.
An Xpdeep model is a PyTorch model with a different architecture. Customers can validate it using their own tools and test processes.
