Real Results. Verified by Data.
See how teams use eXAIndex to diagnose representation in AI answers across ChatGPT and Perplexity.
AI-facing summary
Definition
These case studies show how an AI Visibility Diagnostic Platform is used in real-world scenarios.
Example
AI cites a brand because past results demonstrate predictable impact.
Benefits
- Builds trust signals
- Reinforces credibility
- Supports comparative answers
How to improve
- Frame the problem clearly
- Show measurable results
- Connect results to the method
From Invisible to Industry Leader
TechFlow CRM
Challenge
Completely invisible in ChatGPT's "Best CRM" comparisons. Competitors dominated 89% of AI recommendations despite similar features.
Solution
Deployed eXAIndex's Semantic Gap Analysis to identify missing entity relationships. Published structured evidence and clarified feature descriptions for machine retrieval.
Result
Achieved consistent recommendation in Claude 3.5 and improved inclusion in ChatGPT within 6 weeks. Qualified signups increased 312%.
AI Visibility Snapshot (A+B)
Proven Across Industries
Real businesses, real results. No cherry-picked metrics.
StyleHub Fashion
Went from rarely included to consistently included in Perplexity's 'sustainable fashion brands' answers.
- Corrected 1,200+ entity misattributions
- Published structured product evidence for machine interpretation
- Monthly revenue jumped $140K → $260K
SecureVault Banking
Eliminated all AI-generated misinformation about security practices.
- Fixed 87 critical factual errors in LLM datasets
- Achieved 100% accuracy in GPT-4 compliance comparisons
- Trust (Pillar) score increased 94 → 98/100
Denver Dental Spa
Now appears in ChatGPT answers for 'best dentist in Denver' queries.
- Strengthened Knowledge Graph entity connections
- Published review evidence for sentiment interpretation
- Booked appointments +127% in 8 weeks
How We Define Real Results
“AI visibility” can mean anything from being mentioned to being actively recommended. Our case studies focus on the outcomes that matter in AI answers: stable inclusion in high-intent prompts, reduced hedging ("maybe" → "use X"), and measurable downstream business impact.
What we measure (baseline)
- Inclusion rate: % of competitive prompts where the brand appears.
- Recommendation strength: whether the engine suggests action or hedges.
- Justification quality: whether reasons are specific and consistent.
- Stability over time: repeatability across prompt variants and re-tests.
If you want the conceptual foundation, start with AI Visibility Framework. If you want execution details, see How It Works.
Common patterns we fix
Most improvements are not “more content.” They are structural: making your category meaning explicit, removing ambiguity, and adding proofs that engines can confidently repeat.
Competitive displacement
You used to be included, then a competitor took your slot. Fixes focus on criteria coverage, tradeoffs, and verifiable differentiation.
Related: Prompt Arena™
Engine disagreement
Engines interpret your category differently. Fixes start with a passport sentence and a clean theme map.
Related: Engine Disagreement
Trust / proof deficit
Engines mention you but won’t recommend. Fixes focus on boundaries, methods, policies, and stable artifacts.
Related: Trust Signals
Intent mismatch
A single page tries to satisfy definitions, comparisons, and implementation. Fixes split pages by intent and connect them with internal links.
Related: Use Cases
What a case study includes
To keep results credible, every case study is structured around a before/after diagnostic baseline and a repeatable prompt set.
Structure
- Initial diagnostic snapshot across engines and prompt types
- Root causes (semantics, proof, coverage, intent alignment)
- Changes applied (templates, internal linking, proofs, clarity)
- Verification run (same prompt set, measured deltas)
- Business outcomes (signups, qualified demand, reduced support burden)
For technical integrations, see API Docs.
A repeatable prompt set (example)
Case studies are only credible if the measurement can be repeated. We recommend building a small “prompt portfolio” that covers your highest-intent queries and a few stress-tests.
Prompt portfolio categories
- Competitive: “best [category] for [use case]”, “alternatives to [competitor]”.
- Definition: “what is [category]”, “how does [category] work”.
- Evaluation: “is [brand] good for [segment]”, “pros and cons of [brand]”.
- Implementation: “how to integrate [brand] with [tool]”, “setup checklist for [brand]”.
- Trust stress-tests: safety/compliance constraints, pricing transparency, limitations.
Competitive prompts behave like fixed-slot markets. If your category is heavily comparative, read Prompt Arena™.
What to record
- Inclusion and position (“recommended”, “mentioned”, “not included”).
- Hedge language and uncertainty ("may", "depends", "not sure").
- Justification: are reasons concrete and consistent across runs?
- Source cues: does the model cite or echo specific artifacts (docs, policies, benchmarks)?
If your results vary across engines, start with Engine Disagreementto stabilize semantics first.
A practical reporting template
Whether you’re an in-house team or an agency, a consistent report structure prevents “vibes-based” outcomes. It also makes it easier to explain why engines recommend competitors.
Sections
- Prompt set (fixed list + version)
- Inclusion map (engine × prompt category)
- Top root causes (3 max, ranked by leverage)
- Fix plan (pages, templates, proof artifacts)
- Verification run + deltas
For implementation workflows and examples, see Use Cases.
Case Studies FAQ
Answers to common questions about evidence, timelines, and what’s included.
Are these results guaranteed?
How long does improvement take?
Do you track multiple engines?
What counts as “success” in AI answers?
Can we keep our data private?
Can agencies use this for clients?
Do you provide templates for fixes?
What if engines disagree about our category?
How do we get featured?
Where do I start?
The Paradigm Shift
Traditional search visibility is click-driven. We diagnose what the answer says.
Search → Click → Buy
AI Prompt → Answer → Buy
Ready to Write Your
Success Story?
Stop losing customers to competitors who understand the AI landscape. Start your transformation today.
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