APPLICATIONS

When the model can carry more,the system can do more.

Xpdeep turns structural control at model level into new operating, product and engineering possibilities.

MODEL UNDERSTANDING

FRAME t

BOUNDED DECISION

OPERATIONAL CHANGE

ACT

HOLD

ESCALATE

ILLUSTRATIVE

01 BOUNDED OPERATIONAL DECISIONS

Move routine decisions toward the model.Keep experts for exceptions.

When a model explains its prediction, reports its attributed domain state and, where applicable, provides a minimal feasible intervention under constraints with structural justification, more routine decisions can be handled under customer-defined system rules.

MODEL OUTPUTSCUSTOMER SYSTEM RULES

ACTHOLDESCALATE

ESCALATE →

EXPERT REVIEW

A HOLD CAN RESOLVE UNDER SYSTEM RULES · EXPERT ATTENTION → ESCALATED EXCEPTIONS

Prediction and structural explanation share one forward pass. Prescription is a separate inference, invoked when useful on temporal and tabular stages, in Native and Retrofit.

01 WHAT IS CONSTRAINED TODAY: Routine decisions wait for expert confirmation. Expert headcount grows with the installed base, and response time follows expert availability.
02 WHAT CHANGES AT MODEL LEVEL: The model returns why it predicts and whether it is inside its trained domain, and, where supported, a minimal feasible intervention with its structural justification.
03 WHAT COULD CHANGE: A share of routine cases can be handled under customer-defined rules, so the ratio of machines or decisions per expert can rise. Not every in-domain case becomes automatable.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: Which decisions qualify, the share handled without review, false-hold and escalation rates, and the effect on expert time, against a KPI agreed at scoping.

APPLICATION EXAMPLE · MEASUREMENT CORRECTION

Trace a model error to a recoverable input distortion, then prescribe a signal correction with structural justification.

STATUS

Measurement correction and signal-level localization are live and demonstrable. The impact on expert workload and delegated decisions is measured on the customer’s application.

POTENTIAL OUTCOMES TO MEASURE

Fewer unnecessary reviews
Potentially more assets or decisions per expert
Faster operational response

ACT / HOLD / ESCALATE RUN UNDER THE CUSTOMER’S SYSTEM RULES.

See the decision chain →

02 MATERIAL AND RESOURCE ECONOMICS

Reduce the material and resourcesthat pay for uncertainty.

Some product and process margins compensate for uncertainty about state, loads or distance to a quality limit. Better estimation, structural explanation and attributed domain awareness can help engineering determine which part of that margin can be recovered within physical and engineering constraints.

MARGIN COMPOSITION

ILLUSTRATIVE · NOT TO SCALE

REQUIRED

Physical and engineering requirements, retained
Additional margin held for uncertainty
Portion to evaluate for recovery

01 WHAT IS CONSTRAINED TODAY: Products carry additional material, or processes consume additional resources, because actual conditions cannot be characterized precisely enough. Physical and safety margins remain necessary.
02 WHAT CHANGES AT MODEL LEVEL: A virtual sensor estimates an unmeasured quantity with structural explanation and attributed domain state. For process applications, constrained prescription can provide a minimal feasible intervention and explain its predicted effect.
03 WHAT COULD CHANGE: Engineering may use less or cheaper material where a learned estimate supports a revised design. In operations, material and energy consumption may move closer to target under physical and engineering constraints.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: Estimation accuracy, domain coverage, the engineering basis for any recovered margin, and the resulting material or resource KPI. Product-design changes and operational interventions follow the customer’s validation process.

PRODUCT DESIGN

Virtual sensors and intelligent equipment can characterize quantities, state and loads that previously required conservative design assumptions.

STATUS

Material reduction opportunities identified and validated by client engineering on two programmes.

PROCESS OPERATIONS

XpProcess is being developed to optimize material and energy use under physical and engineering constraints, returning minimal feasible interventions with structural justification and attributed domain state.

STATUS

XpProcess is in development. First phase completed and shown to domain specialists; first industrial engagement in design. Customer energy savings have not yet been observed.

Material and resource economics concern the customer’s product or process. Model frugality concerns compute and memory on the deployment target.

03 OPERATING HEADROOM

Recover performance currently left unusedbecause uncertainty forces conservative limits.

A TRANSFORMATION PATTERN, NOT A PRODUCT.

ILLUSTRATIVE

STATIC OPERATION

STATIC BASELINEAPPROVED HARD LIMIT

STATE-DEPENDENT OPERATION

STATIC BASELINEAPPROVED HARD LIMIT

Expands conditionally within the approved range when state and validity are characterized.

Contracts toward the static baseline as uncertainty increases.

SAME APPROVED HARD LIMIT · CUSTOMER FALLBACK RULES PRESERVED

01 WHAT IS CONSTRAINED TODAY: Static limits are set conservatively because asset state, history, degradation and model validity cannot be characterized precisely enough. Part of the margin covers uncertainty, not physics or regulation.
02 WHAT CHANGES AT MODEL LEVEL: The model estimates relevant asset state, explains the estimate and reports attributed domain state. Where applicable, a separate prescriptive inference provides a minimal feasible intervention under constraints and its structural justification.
03 WHAT COULD CHANGE: Condition-aware operation closer to approved limits while state is characterized, capacity, throughput or availability offers. The static envelope stays as fallback.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: Approved hard limits, the fallback behaviour, how much headroom is usable under which conditions, and whether it holds in the field.

The full lifecycle version, including targeted correction and product-variant evolution, requires Native mode.

POTENTIAL ECONOMIC SURFACES

Usable capacity
Power
Throughput
Efficiency
Availability
Premium performance tiers

Hypothesis under exploration; no delivered operating-headroom result. Commercial and operating outcomes depend on application-specific evidence, customer validation and approved limits.

See industries →

04 MODEL FAMILY EVOLUTION

When the product changes,the intelligence should not always have to start over.

A new sensor, supplier component, hardware revision, geometry or product variant can change the learned system.

In Native mode, structural diagnosis can identify a region requiring correction, and engineers can edit that region while freezing the rest. Whether this reduces the cost of moving intelligence between product variants must be established for the application.

NATIVE MODE · TRANSFORMATION HYPOTHESIS TO PROVE

01 PRODUCT VARIANT N

02 STRUCTURAL DIAGNOSIS

03 TARGETED LOCAL CORRECTION

04 VALIDATE BEFORE RELEASE

05 PRODUCT VARIANT N+1

UNCHANGED REGIONS PRESERVED · REST OF MODEL FROZEN

ILLUSTRATIVE

01 WHAT IS CONSTRAINED TODAY: Each new variant, sensor or supplier component can trigger broad model rework. Engineering effort scales with the number of variants.
02 WHAT CHANGES AT MODEL LEVEL: In Native mode, structural diagnosis locates where the model needs correction; a targeted local edit changes that region while the rest stays frozen.
03 WHAT COULD CHANGE: Less engineering effort per variant and a more controlled product family. Reuse depends on what changed; there is no automatic transfer to any new hardware or sensor.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: What changed between variants, the scope of the correction, accuracy on the new variant and the validation required before release.

OUTCOMES TO TEST

Diagnosis time
Engineering hours and retraining scope
Regression-testing scope
Revalidation effort

Product-variant economics remain a hypothesis to prove. Local correction does not automatically reduce the qualification or certification perimeter.

Explore controlled evolution →

05 PERCEPTION DOMAIN GATE

A perception model should knowwhen the world has moved beyond what it learned.

ILLUSTRATIVE. Whether a visual change moves an input outside the learned domain depends on the model and its training data.

CURRENT STATE · NORMAL-CONDITION EXAMPLE

Inside learned domain

ATTRIBUTED DOMAIN STATE

The model remains in the domain it learned. Structural explanation shows what drove the detection.

PERCEPTION MODEL

→

DOMAIN VALIDITY GATE

→

DOWNSTREAM SYSTEM

With read access to the PyTorch architecture and weights, Xpdeep can structurally analyse the customer’s learned perception model. Supplier-owned models require the supplier’s cooperation and authorized access.

The domain state feeds downstream system decisions, slow down, fall back, request review. Domain state alone does not grant authority; the downstream system applies the customer’s rules.

01 WHAT IS CONSTRAINED TODAY: Perception stacks can fail silently outside the conditions they learned, new lighting, weather, sensor drift or unseen object classes.
02 WHAT CHANGES AT MODEL LEVEL: Attributed domain awareness and novel-class detection, with structural explanation of what moved the input outside the learned domain.
03 WHAT COULD CHANGE: An explicit domain-validity gate for downstream decisions. Structural diagnosis of where the perception model needs work. Targeted correction requires Native mode.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: Detection quality of the gate on representative conditions, false-gate rates, and how downstream rules use the signal.

EVIDENCEDemonstrated on 2D detection, 3D camera, camera, LiDAR fusion and video models; observed impacting ADAS model design. The domain-validity gate is a named customer demand today.

ADAS / AUTONOMY · ROBOTICS

See vision capabilities →

06 AGENTIC DECISION OBJECTS

Orchestrators can route tasks.They still depend on what each model returns.

Today, an orchestrator may receive an opaque prediction from a specialized model. Xpdeep can supply a richer model-level decision object.

ORCHESTRATOR

STANDARD SPECIALIZED MODEL

PREDICTION

XPDEEP SPECIALIZED MODEL

DECISION OBJECT

PREDICTION
STRUCTURAL EXPLANATION
ATTRIBUTED DOMAIN STATE
PRESCRIPTION WHERE APPLICABLE
STRUCTURAL JUSTIFICATION OF THE INTERVENTION

Xpdeep supplies specialized decision objects to the orchestration layer. It does not orchestrate.

01 WHAT IS CONSTRAINED TODAY: When an orchestrator receives only an opaque prediction from a specialized model, that output does not explain the prediction or report attributed domain departure.
02 WHAT CHANGES AT MODEL LEVEL: Each Xpdeep model returns a decision object: prediction, structural explanation, domain state, and prescription with justification where applicable.
03 WHAT COULD CHANGE: Agentic workflows can route, hold or escalate on richer model-level inputs. An additional application surface, not a separate business.
04 WHAT THE CUSTOMER MUST MEASURE OR VALIDATE: How the orchestrator uses the object, which actions it is permitted to take, and the rules for hold and escalation.

LIVE TODAY

Single-model MCP / API access.

IN DEVELOPMENT

Xpdeep Agentic Server (XAS).

Explore MCP / API →

One model architecture.Different ways to change the system.

DECISIONS

Move expert attention toward exceptions; measure the operational impact.

MATERIAL AND RESOURCES

Evaluate margins held for uncertainty. Material opportunities engineering-validated; process and energy outcomes under development.

HEADROOM

State-dependent performance inside approved limits. Hypothesis under exploration.

EVOLUTION

Evaluate the cost of moving intelligence between variants. Hypothesis to prove.

PERCEPTION

Attributed domain awareness on perception models; demonstrated across multiple model types.

AGENTIC DECISION OBJECTS

Supply richer model outputs to orchestrators. Single-model MCP / API live.

START WITH ONE FUNDED DECISION

Start with one decisionyou already fund.

One economic constraint. One agreed KPI. Evidence on your data and constraints.

ONE ECONOMIC CONSTRAINT

Expert time, material cost, resource consumption, withheld performance, variant rework or perception uncertainty: identify a cost the business already carries.

ONE DECISION

A defined model or decision loop, owned and funded by the business today.

ONE MEASURABLE KPI

Agreed at scoping and measured on your data. Identify the possible Phase 2 scope at the same time; use Phase 1 evidence to determine the extension.

Each engagement delivers a concrete model, engineering output or evidence package.

WHICH DECISION OR ENGINEERING CONSTRAINT SHOULD WE ADDRESS FIRST?

BOUND A DECISION

A decision still reviewed manually

Discuss this application →

EVALUATE MATERIAL OR RESOURCE MARGINS

A product or process carrying margin for uncertainty

Discuss this application →