Structured standards for readiness, representation, and performance under constraint.
ToastDeck develops evaluation frameworks tied to readiness. When a framework is fully documented and tested in field — we say so.
See All Frameworks →Frameworks ready for integration.
A standard is a documented, tested, replicable evaluation framework that produces consistent results. When we call something a standard — it is one.
Active Standard
Use confidently. Fully documented and tested. Available for integration or adaptation.
Conditional Standard
Structure is sound, formal documentation being completed. Emerging from research.
Planned Standard
Emerging from research. Structure defined, not yet refined under field conditions.
- Active
Recognition → Recall → Execution
The foundational sequence. Used in all ToastDeck training products.
All Training Products - Active
Encoding / Retrieval / Execution Failure Model
Identifies where training breaks down. Distinguishes between failures to encode, retrieve, or execute.
Custom Training Design - Active
SOMAR — AI Selection Measurement Framework
Measures how AI systems recognize, represent, and select businesses. Tested across ChatGPT, Gemini, Claude, Perplexity. Field version: V1a.
Research Studies · Pilot Reports - Active
Scale Inversion
High AI visibility does not correlate to AI selection. Identified across 23 companies in 8 industries.
SOMAR · Research
- In Dev
AI Recognition vs AI Decision Optimization Model
Formalizes when an AI identifies a business vs. whether it selects one. Foundation for B2Ai certification.
- In Dev
ADDS — AI Governance & Decision Schema
Architecture for how organizations structure, archive, and audit AI decision layers.
- In Dev
LAVP — Local Authority & Visibility Protocol
Signal structure approach for local businesses. Actively being validated in field testing.
- In Dev
Authority Evidence Stack
Methodology for progressively organizing authority signals for GEO and AI selection optimization.
- In Dev
Scenario Pressure Ladder
Methodology for progressively increasing pressure in training scenarios.
Frameworks are the structure of the gap — recognition vs. action, in training and in AI contexts. The same gap. Measured differently.
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If you are interested in shaping or using these standards as they mature, we will share criteria drafts and field study pilots before public release.
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