INDUSTRIAL & PROCESS
Recover the process margin
that pays for uncertainty.
Explain the predicted outcome, characterize its domain and compute a minimal feasible intervention under operational constraints. Evaluate material, energy, yield and quality margins while the plant’s control system retains authority.
MATERIAL · ENERGY · YIELD · QUALITY · THROUGHPUT · UPTIME
THE SURROUNDING CONTROL SYSTEM REMAINS IN CONTROL
01 PROCESS STATE
A process model should show what is driving the outcome now.
On temporal data, Xpdeep exposes not only which variables matter, but which segments and signal morphology carry the model output.
ILLUSTRATIVE PROCESS VARIABLES
- MULTIVARIATE PROCESS INPUTS
- STRUCTURAL READING
- OUTCOME
Signal-level localization helps engineers see where the learned decision is actually being made.
02 PRESCRIPTION
Prediction tells you what may happen.
Xpdeep computes what can change now.
PRIMARY INFERENCE
PROCESS STATE
PREDICTSTRUCTURAL EXPLANATIONCHECK DOMAIN
WHEN AN INTERVENTION IS USEFUL
PRESCRIBEJUSTIFY
SYSTEM AUTHORIZATION · CUSTOMER RULES
ACTHOLDESCALATE
Prediction and structural explanation share one forward pass. Prescription is a separate inference, invoked when useful on temporal and tabular stages.
Xpdeep computes a minimal feasible input change under specified operational constraints. Then it returns the structural justification of why that intervention changes the predicted outcome.
IMPORTANT BOUNDARY
Structural, not automatically causal.
Minimal means minimal feasible under the stated constraints, and optimization procedure, not a unique global optimum.
03 CONTINUITY
A constrained adjustment may help avoid an unnecessary stop, once the plant’s system authorizes it.
TODAY
- PREDICTED PROBLEM
- ALERT
- STOP / HUMAN REVIEW
WITH A BOUNDED DECISION OBJECT
- PREDICTION
- EXPLANATION
- DOMAIN STATE
- FEASIBLE INTERVENTION
- STRUCTURAL JUSTIFICATION
- SYSTEM AUTHORIZATION
- CONTINUE
EXCEPTION
- HOLD / ESCALATE
- HUMAN
POTENTIAL OPERATIONAL CONSEQUENCES
- Fewer unnecessary stops
- Faster response
- More continuous operation
- Fewer unnecessary reviews
- Expert attention focused on exceptions
The surrounding control system remains in control. A computed intervention is an input to it, never an authorization to continue.
04 DESIGN
Training should stop being blind trial and error.
- INITIAL MODEL
- READ LEARNED STRUCTURE
- DIAGNOSE PROBLEM REGION
- TARGETED LOCAL EDIT (NATIVE)
- VERIFY EFFECT
REST OF MODEL FROZEN · DESIGN-TIME EDITING REQUIRES A NATIVE XPDEEP MODEL
Xpdeep can reduce unnecessary inputs, align the model to the true business KPI and adjust complexity only where performance losses concentrate.
105 → 19
INPUT VARIABLES
UP TO 82%
FEWER INPUT VARIABLES AT EQUAL OR BETTER ACCURACY
UP TO 4×
FASTER MODEL DEVELOPMENT
Selected validated results across Confiance.AI and client programmes.
CONSTRAINTS
A feasible intervention must respect the process you actually operate.
ILLUSTRATIVE · 1: CONSTRAINTS THAT DEFINE A FEASIBLE INTERVENTION
1 · FEASIBLE INTERVENTION
Operating limits, actuator ranges and process constraints define which interventions are feasible. The diagram shows this decision space.
2 · PHYSICS IN LEARNING
Separately, physical models and constraints can be integrated into learning. This has been done on a continuous-process project.
05 ECONOMICS
The value is not another prediction.
It is a better operating decision.
ECONOMIC SURFACES TO MEASURE
MATERIAL
Evaluate material consumed to compensate for uncertainty about process state or distance to a quality limit.
ENERGY
Evaluate energy per unit produced through constrained operating adjustments.
YIELD
Less value lost to avoidable process decisions
THROUGHPUT
Fewer unnecessary interruptions
QUALITY
Earlier, better-attributed decisions
UPTIME
More continuous operation where conditions permit
Material and energy economics concern the customer’s process. Model frugality concerns compute and memory on the deployment target. Establish the application’s performance and economics against agreed KPIs.
ONE TRANSFORMATION PATTERN
Recover performance currently left unused because the system does not know enough about its own state.
PROCESS OPERATING ENVELOPE
- STATIC PROCESS LIMIT
- CURRENT PROCESS STATE + HISTORY + DOMAIN STATE
- CONDITIONAL OPERATING REGION
- ACT / HOLD / ESCALATE
- BASELINE PRESERVED
POTENTIAL SURFACES
- Throughput
- Energy efficiency
- Quality
- Production rate
- Availability
Illustrative transformation pattern. Xpdeep does not remove physical or customer-defined hard limits.
The technology is already operating on industrial data.
PLATFORM
Core training, structural explanation, optimization and prescriptive engines are live.
PROCESS WORK
Physical models and constraints have been integrated into learning on a continuous-process project.
VALIDATED RESULTS
Selected validated results across Confiance.AI and client programmes include:
- 105 → 19 input variables;
- up to 4× faster model development.
On time-series data, Xpdeep models achieve accuracy at least equivalent to standard deep learning and frequently superior.
XPPROCESS
Material and energy optimization under physical and engineering constraints, returning minimal feasible interventions with structural justification and attributed domain state.
STATUS
In development. First phase completed and shown to domain specialists; first industrial engagement in design.
INTEGRATION
Xpdeep changes the model.
Not your plant stack.
- SENSORS / ACQUISITION
- DIGITIZED PROCESS DATA
- XPDEEP MODEL
- APC / DCS / MES / CONTROLLER / APPLICATION
APC, process-control and optimization systems remain in place. Xpdeep changes what the learned model returns: structural explanation, attributed domain state and explained constrained interventions.
NO PROPRIETARY TRANSPORT OR LOGGING FRAMEWORK IMPOSED.
Industrial engineering boundaries.
EXPAND
- APC, MPC, DCS and MES remain in place.
- Structural integration requires read access to the PyTorch architecture and weights, or a Native model.
- Operating limits stay with the customer.
- Production integration and target profiling remain customer-specific.
- The customer’s surrounding system defines and authorizes actions.
- Attributed domain state covers the retained model inputs; alone, it does not authorize action or establish system safety.
- Structural justification does not automatically establish physical causality.
- Minimal means minimal feasible under the stated constraints and optimization procedure.
- Targeted editing and controlled correction require Native mode.
- System validation and certification remain with the customer and relevant authority.
- Reduced requalification scope requires application-specific evidence and acceptance.
START WITH ONE PROCESS DECISION
Start with a KPI the plant already measures.
MATERIAL
Material consumed to compensate for uncertainty about process state or a quality limit
YIELD
A decision tied to defects or process quality
THROUGHPUT
A bottleneck or avoidable stop
ENERGY
An operating adjustment tied to energy use
UPTIME
A process state tied to interruption or availability
QUALITY
A model that detects an issue but cannot explain what to change
Run 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.
01 →
One funded decision
A model or decision loop the business already owns.
02 →
Agreed comparison
Data, duration and KPIs defined at scoping.
03 →
Evidence on your application
Side-by-side results using the customer’s validation process.
04
Define the extension
Identify the possible Phase 2 scope at the start; use Phase 1 evidence to determine its scope.