Frameworks

Reusable IP tiered by maturity.

Frameworks are reusable across training and research and documented. Some are active. Some are in development. All reflect the same principle — recognition is not enough.

Active Frameworks →
Framework Maturity

Where each framework stands.

Active

Use confidently. Fully documented. Available for integration or adaptation.

Conditional

Tested in field. Documentation in progress. Structure is sound.

Planned

Concept validated. Not yet formally tested. Invite-only access begins.

Internal

Emerging from research. Structuring begins. Not yet available externally.

Active Frameworks
  • Active
    Core Progression

    Recognition → Recall → Execution

    How people move from seeing the pattern to acting on it — in training and in AI systems. Used across all ToastDeck products.

    All Training Products
  • Active
    Diagnostic

    Encoding / Retrieval / Execution Failure Model

    Identifies where the training system breaks down. Distinguishes between failures to encode, retrieve, and execute.

    Custom Training Design
  • Active
    Measurement

    SOMAR — AI Selection Measurement Framework

    Measures how AI systems recognize, represent, and select businesses in decisions. Tested across ChatGPT, Gemini, Claude, Perplexity. Identifies four failure modes suppressing AI selection.

    Research Studies · Pilot Reports
  • Active
    Pattern

    Scale Inversion

    High visibility does not correlate to AI selection. Identified across 23 companies in 8 industries. Foundation for all B2Ai diagnostic work.

    SOMAR · Research
In-Development Frameworks

Active refinement.

  • In Dev
    Conceptual

    AI Recognition vs AI Decision Optimization Model

    Formalizes the gap between when an AI identifies a business and when it selects one. Foundation for B2Ai certification.

  • In Dev
    Governance

    ADDS — AI Governance & Decision Schema

    Architecture for how organizations structure, archive, and audit AI decision layers. Built for enterprise adoption.

  • In Dev
    Local

    LAVP — Local Authority & Visibility Protocol

    Signal structure approach for local businesses seeking better AI recommendation placement. Being tested in active field markets.

  • In Dev
    Field Tested

    Selection Consistency Matrix

    Measures whether AI systems treat your business consistently across queries, contexts, and question phrasings.

  • In Dev
    Data Pattern

    Authority Evidence Stack

    Methodology for progressively organizing authority signals. Built for GEO and AI selection optimization across 8 industries.

  • In Dev
    Active Refinement

    Scenario Pressure Ladder

    Methodology for progressively increasing pressure in training scenarios. Measures performance decay under increasing constraint.

The ToastDeck Principle

Frameworks are the structure of the gap — recognition vs. action, in training and in AI environments. Different environments. Same underlying gap.

Read the Full Doctrine →