RESOURCES
Go deeperwhere it matters.
Articles, technical notes and evidence on model-level structural control, bounded AI authority and the industrial systems they make possible.
- ARTICLES
- Strategic or conceptual explanations written for a mixed executive / technical audience.
- TECHNICAL NOTES
- Deeper explanations of Xpdeep mechanisms and engineering boundaries.
- EVIDENCE
- Validated figures with their attribution, and an anonymized programme example.
- DOCUMENTATION ↗
- Direct link to the separately managed Xpdeep documentation environment.
EXECUTIVE ENTRY · ARTICLE · STRATEGY
Why prediction is not enoughwhen machines must decide.
A model can be accurate and still leave consequential decisions to a human, and keep machines behind conservative margins, extra sensing and blocked functions.
The constraint is not intelligence alone. It is whether the model can carry a defined decision inside known conditions and hand back when those conditions no longer hold.
8 MIN READ
FOR ENGINEERING READERS · ARTICLE
Post-hoc explanation vs structural model control: what actually changes?
Post-hoc methods analyse a model from outside it. Xpdeep reads learned structure from the model itself, Retrofit for an existing PyTorch model, Native when structure is learned with the prediction, which changes how models are designed, operated and evolved on the machine.
POST-HOC
POST-HOC ANALYSIS
BLACK BOX
versus
STRUCTURAL
STRUCTURAL OUTPUT
READABLE MODEL
SUGGESTED READING SEQUENCE
Suggested reading sequence
01
Why prediction is not enoughArticle · the executive argument
02
The integrated decision objectTechnical note · the output in detail
03
Post-hoc, Native and Retrofit comparedArticle · the detailed comparison
04
Three loops only the model can closeTechnical note · links to Technology Three Loops
05
From assistance to bounded AI authorityArticle · where to start and how to extend
06
Sector applications and validated evidenceIndustries page · evidence on Technology
Resource index
TECHNICAL NOTE
TECHNOLOGY
The integrated decision object
What changes when the model returns more than a prediction?
Prediction, structural explanation and attributed domain state in one forward pass, plus, when useful and supported, a separate prescriptive inference with its justification. The input a machine needs before customer rules decide ACT, HOLD or ESCALATE.
7 MIN READ
Read technical note →ARTICLE
STRATEGY / APPLICATIONS
From assistance to bounded AI authority
Choose one funded decision, agree its KPI and assessment conditions, then use measured evidence to decide whether to expand its scope.
A practical Capture / Expand / Transform progression, with customer constraints, engineering judgment and exceptions explicit. Transformation remains a design horizon, not a precondition.
Industrial · Mission-critical
5 MIN READ
Read article →TECHNICAL NOTE
TECHNOLOGY / ENGINEERING
Three loops only the model can close
How engineers move from reading and editing learned structure to using model outputs and validating targeted corrections between operating periods.
The sequence and boundary of each model-level loop, where Native is required, and why the customer's wider lifecycle and system authority remain separate.
Time series
4 MIN READ
Read technical note →TECHNICAL NOTE
ENGINEERING
Native vs Retrofit
A mode-selection guide: inspect a frozen PyTorch model with Retrofit, or use Native when targeted structural editing, optimization or controlled correction is required.
Assess architecture-and-weights access, the work the team needs to do, and the distinct compute, latency and memory considerations on the intended target.
Embedded
4 MIN READ
Read technical note →TECHNICAL NOTE
TECHNOLOGY
Knowing when the model has left what it learned
How attributed domain state differs from confidence, and how a graded indication can focus an investigation of a changed input.
For temporal data, examine responsible variables and time windows. Unusual does not automatically mean invalid, and in-domain does not automatically mean safe to act on.
On image and video, the offering covers structural explanation, attributed domain state and design-loop capabilities. There is no image-level prescription.
Time series · Vision
3 MIN READ
Read technical note →ARTICLE
APPLICATIONS / OEM
From conservative limits to condition-aware performance
How to distinguish a customer hard limit from a margin held for uncertainty, and assess whether some margin could become conditionally usable.
Dynamic operating headroom is a hypothesis under exploration: characterize the relevant state, define a bounded operating choice and evaluate it against a baseline, with customer hard limits unchanged.
OEM · Embedded · Industrial
3 MIN READ
Read article →ARTICLE
TECHNOLOGY / VISION
Why a heatmap is not a model explanation
A heatmap can be a useful visual check. Structural explanation and attributed domain state address different questions about the model and its input.
Turn a region-level observation into an engineering review without treating a visualization as a causal diagnosis, permission to act or a complete model assessment.
Vision
3 MIN READ
Read article →TECHNICAL NOTE
ENGINEERING / TIME SERIES
What signal-level explanation means
Why a global variable ranking may miss what carried one temporal output, and how to examine its time windows, variables and signal morphology together.
Use localization to focus model, measurement and possible-fault investigations. The approved retained-sensor example shows why engineering judgment cannot be replaced by an average ranking.
Time series · MedTech · Industrial
4 MIN READ
Read technical note →EVIDENCE
Selected validated results
Figures come from different programmes and are not all from one project.
- 105 → 19
- input variables · 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
- Up to 50%
- smaller models
- Independent programme evaluation
- 8% vs 12%
- of ROM: 8% for the Xpdeep model vs 12% for the state-of-the-art model (virtual temperature sensors)
- Unnamed European Tier-1 OEM, on its own embedded target
- 12+
- enterprises on the Xpdeep platform
- Company figure
PROGRAMME EXAMPLE · ANONYMIZED CLIENT PROGRAMME
A sensor a global ranking would have rated low
On a virtual sensor programme, structural analysis identified a sensor that was marginal to overall accuracy but carried the model into a specific predictive region. The client’s engineering team retained it for safety reasons; a global importance ranking would have rated it low.
Input reduction is an engineering decision the customer’s team makes with the structure in view, not an automatic cut.
DOCUMENTATION ↗
Developer documentation
APIs, PyTorch integration, deployment and runtime interfaces.
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