Reports & Outputs

Section Overview
This section explains how to read and interpret your AI LocalRank audit report — understanding scores, findings, and diagnostic outputs.

How to Read Your Report

Report Structure

Your AI LocalRank report is organized into distinct sections, each providing a different view of your AI visibility:

1. OVERVIEW DASHBOARD — High-level summary across all platforms
2. PLATFORM CARDS — Per-platform confidence scores and details (ChatGPT, Perplexity, Gemini, Claude, Grok)
3. MODULE SECTIONS — Detailed diagnostics for each analysis area (Entity, Access, Listings, Schema, AI Policy, Intent)
4. POWER PLAN — Prioritized recommendations organized by fix type

Reading Flow

1. Start with Overview — Get the high-level picture
2. Review Platform Scores — Understand per-platform visibility
3. Explore Modules — Investigate specific diagnostic areas
4. Check Power Plan — See what actions are recommended

Understanding Confidence Scores

What Confidence Means

Confidence scores represent how likely an AI platform is to answer questions about your business accurately and helpfully.

What Confidence Is NOT

• Not a ranking — It does not indicate your position relative to competitors
• Not a guarantee — AI platforms control their own behavior
• Not permanent — Scores change as your data and AI platforms evolve
• Not uniform — Different platforms may have different confidence levels

Score Components

Each confidence score is derived from the DxExA framework:

Confidence = D × E × A

Where:

• D = Discoverability (Can AI find you?)
• E = Evidence (Can AI answer questions?)
• A = Actionability (Can AI help users act?)

Your report shows how each component contributes to the final score.

Confidence vs Visibility

The Distinction

Confidence and Visibility are related but distinct concepts:

Why This Matters

A business can have:

• High confidence, moderate visibility — AI answers accurately when asked specifically, but doesn't proactively recommend
• Moderate confidence, high visibility — AI mentions frequently but with hedging or uncertainty
• High confidence, high visibility — The ideal state: accurate answers and proactive recommendations

Factors That Affect Each

Answer Status Categories

Found / Partial / Missing

For each scenario, AI LocalRank determines an answer status:

Found — AI has sufficient information to answer confidently

• All required data is present
• Sources agree
• Action paths are available

Partial — AI can answer but with gaps or uncertainty

• Some information is missing
• Sources may conflict
• Answer may be hedged

Missing — AI cannot answer reliably

• Critical information is absent
• Too many conflicts
• No confidence in response

Status Distribution

Your report shows the distribution of statuses across scenarios and platforms, indicating where your visibility is strong vs weak.

Drop Reasons & Failure Modes

What Are Drop Reasons?

Drop reasons explain why AI fails to recommend or mention your business for specific intents or scenarios.

Drop Reason Categories

How to Use Drop Reasons

Each drop reason indicates a specific type of fix:

"What If" Scenarios

Purpose

"What If" projections show what AI would likely say if specific issues were fixed.

How They Work

For significant drop reasons, AI LocalRank generates a projection:

Current State: "I don't have reliable information about [Business]'s hours."

What If (hours data added): "[Business] is open today until 9 PM. You can reach them at [phone]."

Interpretation

• What Ifs are projections, not guarantees
• They illustrate the potential impact of fixes
• They help prioritize which issues to address first
• They show what's possible with improved data

Platform-Specific Insights

Why Platforms Differ

Your report may show different scores across platforms because each AI platform:

• Uses different primary data sources
• Weights signals differently
• Has distinct behaviors for uncertainty
• Prioritizes different types of corroboration

Reading Platform Cards

Each platform card shows:

Agreement Meter

What It Shows

The Agreement Meter measures how consistently AI platforms would answer about your business.

Why Agreement Matters

• High agreement suggests stable, reliable AI visibility
• Low agreement indicates conflicts or gaps in your data
• Platform-specific disagreement points to which sources need attention

Diagnostic Flags

What They Are

Diagnostic flags are the top issues affecting your AI visibility, prioritized by impact.

Flag Structure

Each flag includes:

Evidence Views

What Evidence Shows

Evidence views display the actual data that drives your scores and findings.

Types of Evidence

Using Evidence

• Verify findings — Check that the data is accurate
• Identify specifics — See exactly what needs fixing
• Track sources — Know where problems originate
• Confirm fixes — Re-audit to see updated evidence

How to Interpret Low Scores

Low Score ≠ Bad Business

A low AI visibility score does not mean your business is bad. It means:

• AI platforms lack the information they need
• Your data may be fragmented or inconsistent
• Technical barriers may prevent AI access
• Competitors may have stronger digital signals

Response to Low Scores

1. Read the diagnostic flags — They explain what's wrong
2. Check the Power Plan — It prioritizes fixes
3. Focus on high-impact issues — Not everything needs immediate attention
4. Re-audit after changes — Verify improvements

Report Freshness

Snapshot Model

Your report is a snapshot — a point-in-time view of your AI visibility.

Why Snapshots

• AI platforms change frequently
• Your data changes over time
• A snapshot provides a stable reference point
• Multiple snapshots enable trend tracking

Re-Auditing

• After making changes — Verify improvements
• Periodically (quarterly recommended) — Track evolution
• After platform updates — AI behavior may shift