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Stage 02 of 8

MATLAB Layer 1

Machine-assisted perception

Make weak signals in operational and CRM narratives visible at scale while keeping raw text inside the controlled environment.

How it works

  1. A versioned operational dictionary identifies causal language, mechanisms, severity, customer consequence, expedite requests and emergency claims.
  2. MATLAB Text Analytics can add token count, dominant terms and lexical novelty to identify vocabulary drift and candidate dictionary updates.
  3. The extractor emits bounded fields such as work center, why-level, mechanism, priority-review signal and corroboration requirement.
  4. Every percept records source, evidence role and dictionary version; raw narrative bodies and the bag-of-words matrix are discarded.
Human contribution

Judgment and authority

Operations, scheduling, service and governance experts construct, validate and approve dictionary changes.

Machine contribution

Perception and consistency

The perception layer scans far more narrative evidence than an individual planner can continuously read and compare.

Output to the next stage

Auditable percepts and observations suitable for the shared situation model.