AI Visibility & Answer Reality — Diagnosed.
Evidence from real AI answers.
eXAIndex diagnoses what prevents consistent AI visibility, proves it with evidence from observed AI answers, and provides corrective actions you can verify across ChatGPT, Claude, Gemini, Grok, DeepSeek and Perplexity.
Free AI Answer Reality™ preview. Diagnose first. Fix with confidence.
AI-facing summary
Definition
eXAIndex is a diagnostic platform that explains why AI systems include or exclude brands in their answers.
Example
Two similar products exist, but AI consistently recommends only one because its positioning is clearer and easier to explain.
Benefits
- Clarifies how AI interprets your brand
- Explains gaps between search visibility and AI answers
- Shows why competitors are chosen instead
How to improve
- Define your category explicitly
- Align content with AI interpretation patterns
- Verify changes across multiple AI engines
The Problem
If AI doesn’t mention your brand, it effectively doesn’t exist.
When users ask AI systems for “best tools”, “alternatives”, or “is it worth it”, AI shapes the market narrative.
Most brands don’t know:
– if they are mentioned
– how they are positioned
– or why competitors appear instead
Unmeasured AI Visibility
Brands often assume they are visible in AI answers, without ever verifying how AI actually represents them.
Unverified Competitive Displacement
AI may recommend competitors instead — not because they are better, but because their signals are interpreted differently.
Answers Without Attribution
As AI replaces links with answers, brands lose visibility unless they are explicitly referenced.
Detect first. Diagnose second. Fix with evidence. Verify results.
Core Diagnostic Capabilities
These are diagnostic methods inside one Diagnostic Platform — used to observe AI behavior, interpret it, and translate it into corrective actions.
Why AI Doesn’t Recommend You™
A root-cause diagnostic explaining exactly why AI systems exclude, downgrade, or omit a brand in answers.
AI Answer Reality™
Prompt-based testing that reveals how AI systems actually respond to real user questions today.
AI Explanation
Engine-level interpretation explaining how AI systems understand, categorize, and reason about a brand.
Strategic Report
Actionable corrective steps outlining what to fix, implement, and improve to increase AI visibility and trust.
The Solution
Diagnosis & Repair Infrastructure
Built to observe, diagnose, and verify — not to guess or blindly optimize.
Our system executes controlled, repeatable prompts across multiple AI engines, then translates observed behavior into concrete diagnostic findings and fixes.
Multi-Agent
Engine
Analysis Modules
Independent diagnostic modules identifying root causes across AI visibility, content, technical, semantic, and trust signals.
Multi-threaded Execution
Efficient execution across engines and prompts, designed to scale diagnostics without unnecessary API cost.
Diagnostic Scoring Logic
Deterministic scoring used to quantify the impact of identified issues — separate from raw AI answers.
Use Cases
Where AI visibility needs a ground truth
Understand AI comparisons
When users compare you to competitors in AI answers, we diagnose how those comparisons are formed — and what signals are driving them.
Key Features
Everything required to diagnose and repair AI visibility
Not a tool for influence — a system for evidence-based fixes and verification.
Interpretation Reports
Executive summaries showing how AI engines present your brand (mentions, citations, comparisons) — as observed, with structured interpretation.
Competitive Reality
See how AI engines compare you vs competitors in “alternatives”, “best X”, and head-to-head prompts — without guessing why.
Structural Readiness Benchmark
A normalized benchmark for structural readiness across engines and markets — separate from live AI answers.
Ready to diagnose and fix AI visibility?
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Semantic Completeness
Close the missing answers gap
AI engines tend to prefer pages that include common blocks: a clear definition, step-by-step guidance, concrete examples, benefits, and an FAQ. Adding these improves understanding and makes your content more eligible for use in AI answers.
Definition
AI visibility is the degree to which AI engines can correctly understand, describe, compare, and cite your brand when users ask real questions. Practically, it depends on whether your site makes your entity unambiguous (who you are), your offering concrete (what you do), and your positioning consistent (how you differ).
- What: category + product/service boundaries (what you are / are not)
- How: how it works at a high level (3–5 steps)
- Why: outcomes (time saved, fewer errors, higher confidence)
- For whom: ICPs, use-cases, and decision criteria
How-to (steps)
- 1Define the entityAdd a clear definition of what you are (category + differentiator), who it is for, and what outcomes you enable.
- 2Align to user intentDescribe the top 3–5 intents users have (compare, evaluate pricing, integrate, troubleshoot) and link each intent to the right section.
- 3Add examples with specificsInclude concrete scenarios, numbers, and sample questions. AI systems prefer specific, bounded examples over abstract claims.
- 4List benefits as outcomesState benefits as measurable outcomes (time saved, fewer errors, higher confidence), not as vague marketing adjectives.
- 5Verify and keep stableRe-run diagnostics after publishing changes and keep the core definition and claims stable across pages (home, pricing, docs).
| Block | What it answers | Example signal |
|---|---|---|
| Definition | What you are / for whom | Entity clarity |
| How-to | How to use / implement | Intent match |
| Examples | What “good” looks like | Depth signals |
| Benefits | Why it matters | Outcome framing |
| FAQ | Objections / edge cases | Coverage completeness |
Examples
Use examples with real prompts and specific outcomes. It helps both users and AI models map intent → answer.
Benefits
Benefits should be outcome-oriented. The goal is to reduce ambiguity and increase the probability your content is selected in AI answers.
- Higher consistency of how AI describes your brand across prompts and engines.
- Fewer “wrong category” or “wrong competitor” comparisons in AI answers.
- Better intent match: users get actionable guidance instead of vague summaries.
- Improved lead quality: visitors self-qualify faster with clear definitions and examples.
FAQ
Common questions that AI and users look for on high-converting pages.
It is different. Traditional search visibility is click-driven. AI visibility is about whether AI engines can correctly understand, compare, and cite your brand when people ask real questions. The outputs are observed AI answers, plus diagnostics and verification through re-runs.
A short, explicit explanation of what your product/service is, who it is for, and what problem it solves—preferably with concrete nouns, boundaries (what it is NOT), and one or two examples.
Not necessarily. A single strong page can work if it contains the expected building blocks (definition, steps/how-to, examples, benefits, and FAQ) and is kept consistent with your pricing/feature claims.
Re-run the same prompts and compare: (1) entity clarity, (2) intent match, (3) citations/mentions, and (4) whether the missing elements score decreases. Consistency over multiple prompts matters more than a single answer.
See how AI actually represents your market
AI Answer Reality™ preview.
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