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CEP Overlays — Consumer Experience Plane Projection Model

1. Purpose​

The Consumer Experience Plane (CEP) Overlay Model defines how a single, invariant product consumption lifecycle (SSCF) is interpreted differently across personas and interaction modalities, without altering the underlying flow.

It enables:

  • A unified consumption model across all product types (Data, AI, and future HDIP aligned products)
  • Persona-specific interaction without duplicating flows
  • Separation of lifecycle structure from experience semantics

2. Core Principle​

Consumption Flow is invariant. Experience is polymorphic.

  • SSCF (Self-Service Consumption Flow) defines how consumption happens
  • CEP Overlay defines what consumption means for a given persona

3. Formal Definition​

A CEP Overlay is:

A semantic projection layer that maps lifecycle nodes in SSCF to persona-specific interaction models, value interpretations, and usage patterns.

4. Architectural Pattern​

4.1 Separation of Concerns​

LayerResponsibility
SSCFDefines lifecycle (intent → discovery → access → experience → value → feedback)
CEP OverlayDefines persona-specific interpretation of each step
ProductProvides capabilities via ports
PlatformResolves, governs, provisions, and observes

4.2 Conceptual Model​


SSCF (Invariant Graph)
↓
CEP Overlay (Persona Projection)
↓
Consumer Experience (UI / API / Workflow)

5. CEP Personas​

The CEP model defines five canonical personas:

5.1 Business Persona​

Intent Type: Decision-making, outcome-driven
Interaction Model: Low technical abstraction
Primary Focus: Value, outcomes, KPIs

AspectInterpretation
ExperienceDashboards, reports
ValueROI, business impact
TrustExplainability, governance
InteractionGuided, simplified

5.2 Analytical Persona​

Intent Type: Exploration, analysis
Interaction Model: Query-based
Primary Focus: Data utility and insight

AspectInterpretation
ExperienceSQL, notebooks
ValueData utility, insight generation
TrustData quality, lineage
InteractionFlexible, exploratory

5.3 AI / Data Science Persona​

Intent Type: Model building, experimentation
Interaction Model: Pipeline-driven
Primary Focus: Feature engineering, model performance

AspectInterpretation
ExperienceFeature pipelines, training workflows
ValueModel uplift, accuracy, performance
TrustEvaluation metrics, fairness
InteractionIterative, experimental

5.4 System / API Persona​

Intent Type: Programmatic integration
Interaction Model: API / event-driven
Primary Focus: Reliability and performance

AspectInterpretation
ExperienceAPIs, streams
ValueThroughput, latency, availability
TrustSLA/SLO, contract reliability
InteractionDeterministic, automated

5.5 Agentic / Workflow Persona​

Intent Type: Orchestration, reasoning, automation
Interaction Model: Workflow / agent-based
Primary Focus: Outcome execution across steps

AspectInterpretation
ExperienceWorkflow orchestration, agent execution
ValueOutcome efficiency, task completion
TrustSafety, bounded autonomy
InteractionDynamic, multi-step, adaptive

6. Node-Level Semantic Projection​

CEP overlays operate at the node level of SSCF.

Example: “Experience” Node​

PersonaInterpretation
BusinessDashboard
AnalyticalSQL / query execution
AIFeature pipeline
SystemAPI / stream
AgenticWorkflow orchestration

Example: “Views” Node​

PersonaInterpretation
BusinessAggregated KPIs
AnalyticalJoined datasets
AIFeature datasets
SystemJSON / event payloads
AgenticWorkflow outputs

Example: “Usage Signals”​

PersonaInterpretation
BusinessConsumption patterns
AnalyticalQuery logs
AITraining usage
SystemAPI calls
AgenticWorkflow runs

Example: “Value”​

PersonaInterpretation
BusinessROI
AnalyticalData utility
AIModel uplift
SystemService performance
AgenticOutcome efficiency

7. Relationship to Product Types​

The CEP Overlay model is product-type agnostic.

It applies uniformly to:

  • Data Products
  • AI Products
  • Composite Products (AI + Data)
  • Future Product Types

Key Principle​

Product type does not define consumption flow — persona and intent do.

8. Relationship to AI Products (AIPS)​

CEP overlays fully accommodate AI-specific consumption characteristics:

8.1 Decision-Centric Consumption​

Handled via:

  • Business persona (decision support)
  • Agentic persona (automated reasoning)

8.2 Iterative Interaction​

Handled via:

  • AI persona (experimentation)
  • Agentic persona (multi-step execution)

8.3 Trust and Risk Awareness​

Handled across all personas via:

  • Trust signals (AIPCH06)
  • Safety controls (AIPCH19)
  • Oversight (AIPCH21)

9. Relationship to AIPCH Characteristics​

CEP overlays operationalize multiple AIPCH characteristics:

AIPCHConnection
AIPCH12 (Consumption Intent)Persona defines intent context
AIPCH20 (Composable)Agentic persona enables composition
AIPCH21 (Human Oversight)Business persona emphasizes control
AIPCH19 (Safety)Agentic/system personas require enforcement
AIPCH08 (Reusable)Same product reused across personas

10. Key Design Outcomes​

10.1 Unified Consumption Model​

  • One SSCF
  • Multiple interpretations
  • No duplication

10.2 Reduced Cognitive Load​

Consumers interact in familiar paradigms:

  • Business → dashboards
  • Engineers → APIs
  • Data scientists → pipelines

10.3 Future-Proof Architecture​

New personas or modalities can be added without:

  • changing SSCF
  • changing product model

10.4 Ecosystem Enablement​

Products become:

Composable capabilities consumed across multiple contexts

11. What CEP Overlay Is Not​

CEP Overlay is not:

  • A UI theme switch
  • A different consumption flow
  • A product-specific customization

CEP Overlay is:

A semantic reinterpretation layer over a fixed lifecycle

12. Canonical Statement​

The Consumer Experience Plane (CEP) Overlay Model enables a single, invariant product consumption lifecycle (SSCF) to be interpreted across multiple personas and interaction modalities by projecting node-level semantic meaning without altering the underlying flow, thereby supporting unified, scalable, and product-agnostic consumption of Data and AI Products.