Healthtech AEO engagement · Case study
How NoorRank rebuilt the AI-visibility foundation for Nura by FUJIFILM, India's first AI-powered preventive health screening centre.
Verified starting point
"what is nura?"
AI engines returned an anime title, or an audio technology brand. Nothing about health screening.
Verified today
"what is nura?"
ChatGPT, Gemini and Brave now identify Nura correctly: FUJIFILM DKH LLP's preventive health screening centre, citing nura.in.
Nura by FUJIFILM entered the Indian market as a category-defining idea: full-body preventive health screening, powered by AI imaging, built by a joint venture between Fujifilm and Dr. Kutty's Healthcare. On paper, it is exactly the kind of brand an AI answer engine should recognise and recommend easily.
In practice, the name collided with an unrelated anime character and an established audio technology brand, also called Nura. Ask ChatGPT, Gemini or Perplexity a simple question like "what is Nura", and at the point NoorRank was brought in, the honest answer was that the AI genuinely did not know.
For most brands that is an annoyance. For a healthcare brand, it is closer to a floor problem. AI answer engines apply a higher trust bar to health information, the same experience, expertise, authority and trust signals search engines have long weighted in medical results. If the entity itself is not clearly and consistently defined, nothing built on top of it can be trusted or cited.
NoorRank ran a full audit of nura.in, combining an in-house AEO framework with standard technical validators: schema testing tools, crawl simulators, and direct queries against Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Copilot and Gemini. Every finding had to be checkable, live on the site or in a validator. Nothing went into the report that could not be independently verified.
The findings sorted into three groups.
Structured data existed across the site, but carried errors that worked against correct identification: an incorrect medical classification, incomplete organisation details, and page types that contradicted the site's own copy about what Nura does and does not do.
Key facts, including pricing and service descriptions, were stated differently in different places. An AI system trying to summarise Nura in one clean answer had no single version of the truth to draw from.
Elements that render correctly for a human visitor, animated counters, dynamically loaded sections, returned empty or inconsistent data to the crawlers and AI retrieval systems that decide what gets cited.
Phase 1 focused on infrastructure NoorRank could control directly.
Checked directly against nura.in in August and September 2026, each figure below is reproducible via view-source or Google's Rich Results Test.
To test this honestly, NoorRank ran a query that never names Nura, logged out, on a clean session: "Where can I get an AI-powered full body health checkup in India?"
Named as the closest match, shown with a map card
Named in the opening sentence
Every centre listed with its neighbourhood, each cited to nura.in
Used as the benchmark for describing other providers
The results above trace directly back to specific work, not general optimisation.
Built: llms.txt
ChatGPT described Nura as "a dedicated preventive health screening centre, not a hospital, clinic, or treatment facility." That is the exact language written into nura.in/llms.txt during this engagement.
Built: clinic schema, six centres
Gemini named Bengaluru (Indiranagar), Gurugram (Sector 43), Mumbai (Worli) and Hyderabad (Madhapur) by neighbourhood. These are the exact values written into the clinic markup for each centre.
Built: hours and specialty schema
ChatGPT displayed a Nura centre as a map result showing live open or closed status and its medical imaging category, drawn straight from the opening-hours and medical-specialty fields written during this engagement.
Healthcare content carries a different bar, and NoorRank's schema and content decisions reflect that throughout.
Rather than label Nura with a generic "preventive medicine" tag, its medical specialty is coded as public health, linked to the clinical concept of preventive healthcare. That keeps AI systems from misreading Nura as a diagnostic lab or clinic, which it explicitly is not.
Review attribution stays at the organisation level, "Reviewed by the NURA Clinical Team", never a named individual, matching how the review process actually works.
Commercial search terms get the same care. "Full body checkup" becomes "comprehensive preventive screening" in the copy; "best health checkup centre" becomes language Nura can responsibly stand behind. The keywords still get matched. The claims stay honest.
Every finding is reproducible via view-source, Google's Rich Results Test, or any public crawler.
Nura's team works best in the room, not through documents landing cold in an inbox. Engagement runs on weekly live sessions: findings and fixes get walked through together, decisions get made on the spot, with short recap notes following rather than long written reports.
Every deliverable is built for two audiences at once, strategic framing for the people deciding, and operational detail for the people shipping the change.
Nura's entity-resolution and structured-data foundation is built, live and verified. What started as an AI answer engine unable to identify Nura at all now returns Nura first, correctly named and correctly attributed, for a cold, category-level search.
That result sits on top of infrastructure that is still growing: deeper structured data, wider coverage across review and location pages, and a stronger presence on AI platforms beyond Google's surfaces, where Nura's visibility is currently concentrated.
Infrastructure sets the floor. Authority sets the ceiling.
If AI answer engines cannot correctly identify or cite your brand, that is fixable. It was for Nura.
Talk to NoorRank