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+# Value Creation Model
+
+## Purpose
+
+Explain how the company creates measurable or directional value for local service businesses.
+
+## Core Value Thesis
+
+The company creates value by reducing Silent Customer Loss through continuous monitoring, diagnosis, prioritization, and approved low-risk optimization of digital pathways that affect discovery, trust, and customer action.
+
+## Value Categories
+
+### Customer Path Leakage
+
+Customer Path Leakage occurs when a customer has interest or intent but fails to contact, book, visit, or otherwise engage the business because of digital friction.
+
+This category often produces **verified evidence** because the issue can be tied to a concrete defect, timestamp, platform state, or measurable artifact.
+
+### Discovery Failure
+
+Discovery Failure occurs when a potential customer never meaningfully encounters the business because customers, search engines, or AI systems do not find, understand, surface, or accurately represent it.
+
+This category often produces **indicative evidence** because absence or weak representation in search or AI-assisted discovery can be sampled and demonstrated, but not always proven as a direct lost customer.
+
+## Evidence Tiers
+
+### Tier 1: Verified Evidence
+
+Verified Evidence is based on concrete, timestamped, observable, or instrument-backed findings.
+
+**Examples:**
+- Wrong phone number
+- Incorrect business hours
+- Broken booking link
+- Broken contact form
+- Incorrect listing data
+- Indexing problem
+- Metadata problem
+- Canonical tag issue
+- Missing or incorrect Google Business Profile information
+
+**Reporting standard:**
+
+Verified Evidence may be presented as a confirmed issue.
+
+### Tier 2: Indicative Evidence
+
+Indicative Evidence is based on sampled, directional, or probabilistic findings that suggest risk or opportunity but do not prove direct customer loss.
+
+**Examples:**
+- AI answer absence
+- AI misrepresentation
+- Weak entity clarity
+- Inconsistent service descriptions
+- Thin FAQ coverage
+- Missing structured data
+- Competitors appearing more consistently in AI answer samples
+- Weak local discovery signals
+
+**Reporting standard:**
+
+Indicative Evidence must be presented as directional risk or opportunity, not as confirmed customer loss.
+
+## Reporting Principle
+
+Reports must distinguish between confirmed issues and directional risks.
+
+The company should not overstate indicative findings as proven customer loss.
+
+## Before/After Proof
+
+Customer Path Leakage may support stronger before/after proof.
+
+Discovery Failure may support directional before/after comparison, but should be labeled as sampled or indicative when appropriate.
+
+# Evidence Tier Assignment Authority
+
+Agents may propose an evidence tier for a finding, but the final evidence tier used in any client-facing report must be approved by a human reviewer during Version 1.
+
+A finding may only be classified as Verified Evidence when it is supported by concrete, current, observable, timestamped, or instrument-backed evidence.
+
+If a finding does not clearly meet the standard for Verified Evidence, it defaults to Indicative Evidence until a human reviewer confirms otherwise.
+
+Ambiguous findings must not be presented as confirmed customer loss.
+
+If an agent proposes a finding as Verified Evidence and the human reviewer rejects that classification, the finding defaults to Indicative Evidence unless the reviewer discards the finding entirely or requests additional evidence.
+
+**Examples:**
+
+- A currently visible wrong phone number on a verified business profile may qualify as Verified Evidence.
+- A broken booking link observed and timestamped during review may qualify as Verified Evidence.
+- A stale or unconfirmed citation mismatch defaults to Indicative Evidence until confirmed.
+- Absence from sampled AI answers is Indicative Evidence.
+- Agent-reported findings without supporting artifacts are not sufficient for Verified Evidence.
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