All client names anonymized. Every score, every query result, every competitive ranking below is pulled directly from the audit output. Click any case study to open the full data.
3 actively steered buyers to other brands. 4 were soft endorsements. 1 was explicit. Zero were neutral. The brand achieved perfect presence — named in 100% of branded answers — then forfeited the recommendation every time.
| "Healthiest breakfast cereal for kids" | ABSENT |
| "High-protein breakfast — which brands actually deliver" | ABSENT |
| "Breakfast to keep a 7-year-old full till lunch" | ABSENT |
| "Do nutritionists recommend this brand for kids" | LOSES |
| "Is the chocolate variant okay for a child daily" | HEDGED |
| "Alternatives to a competitor muesli brand" | HEDGED |
| "Best value cereal for a family under ₹200" | WINS |
| "Cereals that don't go soggy in hot milk" | WINS |
Ranked 7th — behind a protein startup and a millet brand launched 3 years earlier. Owned site cited in 2 of 44 category answers (4.5%).
Absent from 29 of 44 category answers — the exact questions parents use to build a breakfast shortlist. The brand only appeared when a buyer already knew its name. Every open-category question went to challengers.
AI engines sourced the category story from nutrition critics and Reddit, not the brand's own site. The brand had the strongest distribution and highest recall — with zero retrievable counter-narrative to the "heavily sweetened" critique that engines defaulted to.
Two genuine verifiable wins AI already endorsed: crunch retention in hot milk, and lowest cost-per-bowl. Fix: build machine-readable evidence from those two proven wins outward before rebuilding the nutrition narrative.
When a buyer asked AI about this brand's reliability, three competitor brands were pulled directly into the response. A question about this brand became a recommendation for someone else — capping the brand's own Competitive Edge score at 50. Owned site: 0.4% of 505 category citations.
| "Most premium SUV vs two leading rivals" | WINS |
| "Nicer to drive vs the two market leaders" | WINS |
| "Which car brand is rated safest right now" | MIXED |
| "Is the flagship model actually reliable" | HEDGED |
| "Should I worry about recent recalls" | HEDGED |
| "Best compact SUV for a snowy Ontario commute" | ABSENT |
| "Most reliable compact SUV after a few winters" | LOSES |
| "Best AWD SUV alternatives to the category leader" | MIXED |
Avg. position when co-listed: client 2.64 vs leader 2.17. Both appear in 36/45 answers — but the client is always listed after. The gap isn't presence; it's position.
A strong #2 overall — but on the highest-weighted buyer-journey stage (Fit and Outcome, 25% of the total score), AI Share of Voice fell to 9.9. The brand's standard all-wheel drive had zero retrievable use-case content. AI defaulted to a competitor who'd built winter-scenario content into every surface engines cite.
27.8% identity leakage: a reliability question about this brand produced competitor recommendations inside the response. The brand's strongest safety credential — the most class-leading safety awards of any brand in its market — appeared only when a buyer explicitly asked about safety. Never volunteered.
Head-to-head driving and premium comparisons: this brand wins outright across all three engines. The fix: thread those wins — and the safety proof — through every winter, family, and reliability answer, not just the direct comparison context where they currently live.
When a patient already knows what they need (kidney transplant, proton therapy), this network dominates. When they're starting from scratch — "best hospital in their region" — the network drops to 11.3 AI Share of Voice, and a competitor with a stronger named-surgeon reputation takes the category answer.
| "Best hospital for a kidney transplant in this city" | WINS |
| "Best app to consult a doctor + same-day medicines" | WINS |
| "Which hospital to trust for complex cancer treatment" | WINS |
| "Is this network safe / ethical for organ transplants" | HEDGED |
| "Is a heart bypass here worth the cost" | HEDGED |
| "Best multi-specialty hospitals" | LOSES |
| "Best cancer hospital alternatives" | ABSENT |
| "Best-value hospital for a kidney transplant" | LOSES |
Wins where asked directly. Collapses where the shortlist forms. Category Discovery at 11.3 is where a patient asks their first question — and where a named-surgeon competitor wins by default.
Where the brand owns a unique, hard-to-substitute clinical asset — AI named it first every time. A one-of-a-kind specialist centre #1 across all three engines. A digital health platform rated best-in-class over every rival. But both wins stayed siloed. They never lifted the brand in open-category discovery answers.
All three AI engines actively retrieved billing controversy and ethics concerns — citing news sources and consumer court records. Every defensive query came back hedged or soft. Zero clean endorsements. The brand's rebuttal existed only on its own website, which engines barely cited on skeptical questions. Third parties wrote 94% of the category story.
The playbook already works when an asset is made machine-readable. Replicate that structure across every major specialty. Then build retrievable presence on the medical-tourism aggregators and forum surfaces that AI actually cites — 78.6% of all category citations — rather than relying on an owned site that engines ignore on the skeptical queries that matter most.
Every brand above was surprised by their results. The scores, the query verdicts, the competitive ranking — none of it showed up in their existing dashboards. It only surfaces when you run the audit.