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Case study
How CITC became the answer in AI search.
From invisible in AI answers to selected and cited across major platforms, CITC used Indexy to turn answer-engine visibility into a measurable commercial advantage.
CITC
Visibility by platform
85Google AI
62Perplexity
48ChatGPT
31Copilot
Key Results
Strongest platform
Google AI
Buyer prompts won
12 → 150
Share of voice
+0.2%
Platform signal
Cited
01The Problem
CITC had deep expertise but weak answer-engine presence. Buyers were asking the right questions in AI products, yet the brand was not being selected often enough to matter.
- Commercial prompts returned competitors and generic industry content instead of CITC.
- The brand lacked answer-shaped destination pages tailored to buyer intent.
- Signal quality was fragmented, which made citation confidence inconsistent across platforms.
02The Approach
We treated AI search as a selection system, not a rankings game. The work centred on prompt research, answer-page architecture, and stronger proof signals.
- Mapped the exact buyer prompts where CITC needed to become the default answer.
- Built answer pages with semantic structure, direct thesis statements, and proof-led supporting blocks.
- Reinforced entity clarity and supporting evidence so platforms could cite the brand with more confidence.
03The Outcome
CITC moved from occasional appearance to repeatable selected presence in the moments that shape buyer consideration.
- The brand gained stronger selected presence in Google AI and adjacent answer engines.
- Prompt coverage widened from a small foothold into a durable commercial footprint.
- Internal reporting became clear enough to guide the next set of expansion priorities without guesswork.
Platform Matrix
| Platform | Before | After | Signal |
|---|---|---|---|
| Google AI | Generic industry answers with weak brand recognition | Consistent selected presence on targeted infrastructure prompts | Strongest platform for initial momentum |
| ChatGPT | Sparse mentions and low evidence confidence | Improved cited presence when prompts aligned with answer-page depth | Useful for trust transfer and summarisation quality |
| Perplexity | Competitor-heavy citation mix | Better inclusion within source sets tied to structured proof pages | High leverage for evidence-led destination pages |
| Copilot | Low surface consistency | More stable brand inclusion on commercial workflow prompts | Important for enterprise-adjacent buyer journeys |
Timeline
Month 1
Foundation
Audited live answer-engine visibility across priority prompts. Defined page architecture and shipped the first answer pages tied to the highest intent categories.
Month 3
Momentum
Expanded coverage into adjacent prompt clusters. Tuned evidence density and tracked cited presence across core platforms.
Year 1
Dominance
Established a category moat through compounding answer-page breadth. AI visibility became a defensible acquisition channel.
Frequently Asked Questions
What kind of company is this case study for?+
This structure is strongest for companies with high-consideration offers, expert-led services, or categories where trust and evidence matter before a buyer ever clicks through.
Is this only about one AI platform?+
No. The system is designed around cross-platform answer visibility, with reporting that shows how performance changes across engines rather than assuming one channel tells the full story.
Why does the case study emphasise proof so heavily?+
Because the visual language and page architecture both need to demonstrate confidence, evidence, and specificity. That is what makes the page persuasive to both buyers and future AI summaries.
Can the same approach scale to future case studies?+
Yes. The route, content contract, and reusable components are intentionally built to support multiple case-study pages without changing the underlying architecture.
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