Site index for AI agents: /llms.txt. Documentation pages under /docs are also served as markdown at the same URL plus .md (e.g. /docs/geo-audit.md) or via an Accept: text/markdown request.

Case studiesReal estate

Residential developer, Bengaluru · AirPulse analysis

How a Bengaluru developer gets named when buyers ask AI about new launches

Home buyers in Bengaluru now ask ChatGPT, Gemini and Perplexity which projects to shortlist. We tracked what the engines answer for a residential developer and found the one question type where it already wins: upcoming projects in its localities, where it was named in 1 of every 5 answers. The play is to turn that into every locality it builds in.

20.7%
Answers naming it on new-launch questions for its localities
25
AI answers already citing its blog as a source
6.4
Developers named in a typical locality answer

The challenge

Buyers asking AI about Bengaluru property rarely name a builder, and the engines answer with portals, news and other developers' blogs. The developer's own content was being used as a source in 25 answers, yet it was named in only 2 of them.

What we found

New-launch questions are the way in: asked for upcoming projects in its localities, the engines named the developer in 12 of 58 answers (20.7%), Sep 9 to 28, 2026.
Its content already feeds AI answers: its site was cited in 25 answers, 23 of them through one blog post on investment areas, but the developer was named in only 2.
Portals and other developers' blogs supply more than half of all citations in its market, so the facts on those pages decide what buyers hear.

The play

  1. 1Publish a page for every project and locality with price band, RERA number and possession date, so engines have specific facts to name.
  2. 2Rewrite buyer guides so the developer and its projects sit inside the answer text, turning 25 uncredited citations into mentions.
  3. 3Keep portal listings consistent with the site, since portals supply half the citations on locality questions.
  4. 4Fix the project schema and publish Organization data and an llms.txt, found missing in our site check on 2026-08-17.

Where AI finds its answers in this category

Competitors' sites28.6%
Listing portals25.9%
Blogs and guides19.8%
Other12.2%
News and media7.1%
Government and regulator3.7%

See how we work with brands like this one: AI visibility for real estate.

How we measured

A Bengaluru residential developer was evaluating AirPulse. AirPulse tracked five home-buyer prompts that do not contain any developer's name, once a day on four AI engines with India as the region, from 2026-09-09. The question was simple: when a buyer asks AI about Bengaluru property without naming a builder, does this developer come up?

5 tracked buyer questions, 20 daily runs, Sep 9, 2026 to Sep 28, 2026, on ChatGPT, Gemini, Perplexity, Google AI Overviews. Published without the brand's name.

  • Across all five unbranded buyer questions the developer was named in 14 of 290 answers (4.8%) on ChatGPT, Gemini and Perplexity, Sep 9 to 28, 2026.
  • Small sample: 20 daily runs over 20 days on 5 prompts. Tracking began on 2026-09-09, so there is no earlier baseline to compare against.
  • A Google AI Overview scraper outage from 2026-09-12 to 2026-09-28 overlaps most of the window. Google AI answers returned fewer citations (6.3 per answer in the week of 2026-09-14, 3.8 in the week of 2026-09-21), so mention rates exclude Google AI.
  • Naming is measured by text match on the answer, counting project names that carry the developer's name. The platform's stricter brand-name match counts fewer mentions.
  • Source categories are assigned by domain. Broker and channel-partner project sites count as listing portals; 'Other' is mostly Google Maps and search links, image hosts and document-sharing sites.
  • This was an evaluation. No fixes by AirPulse on the developer's site are documented, so nothing here is an outcome of AirPulse work.

See where AI recommends you.

Book a free strategy call and we run your buyers' questions on the engines while you watch.