Kat3x — AI Visibility ObservatoryIT

Semantic Structures (Real Cases)

Industrial templates ready for Knowledge Seeding. Discover how unstructured data is translated into high-assimilation formats for AIs. Each example has been validated on 5 different LLMs (GPT-4o, Claude 3.5, Gemini 2.5, o1, Llama 3.1) to ensure cross-model parsability.

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An artificial intelligence cannot cite what it does not understand. And it cannot understand what is not structured.
Canonical Definition (Kat3x Principle)

Templates by Industrial Sector

Manufacturing — Machine Configuration
USE CASE

Representation of an agricultural industrial machine. Structured to ensure the LLM understands technical specs (like production capacity) while ignoring marketing noise. This template has been tested with Copilot and Claude to verify technical accuracy in procurement decisions.

@entity: machine
@id: dryer_tx_500
@context: industrial_agriculture

machine{type|input_moisture|output_moisture|capacity}:
  type: grain dryer
  input_moisture: 24%
  output_moisture: 14%
  capacity: 5.3 t/h
  power_req: 45kW
  
@claims:
  - "Reduces moisture by 10% uniformly"
  - "Fully compatible with CHKCD telemetry standard"
Hospitality — Hotel Services Discovery
USE CASE

How to structure luxury Hotel services to ensure 'AI Citability' during user prompt-based discovery (e.g., 'Find me a hotel with a SPA and EV charging').

@entity: hospitality_facility
@id: grand_hotel_veneto
@context: luxury_tourism

facility_services{category|status|access}:
  spa_wellness:
    status: active
    access: guests_only
    features: [sauna, thermal_pool, massage]
  ev_charging:
    status: active
    connectors: [type_2, ccs]
    power_kw: 22

@limitations:
  - "SPA access requires prior booking"
  - "EV charging subject to availability"