AI visibility in Bangalore: four small domains took the whole field, and Bengaluru is the other half of your name

Bangalore gave the most local result of any city we measured in India. Six buying questions, put to ChatGPT and then to Gemini on 15 and 16 October 2026, make 12 readings in all. A source came back in 11 of them. Not one of them pointed at us, 0 mentions out of twelve. What they pointed at instead was onecity.co.in in 4 readings, aiseo.solutions in 4, niyamkavach.com in 3 and nicodigital.com in 3. No global directory in that list. No platform documentation either. Four small sites took the whole field, and not one of them has the city's name in its domain. In this city the specificity that gets cited lives in the pages, not in the web address, and there are two versions of the city's name that both have to lead back to you.

The Bangalore row, read closely

Two domains reached 4 readings each out of 12, which is the highest local concentration in the three Indian cities we read. Compare the other two city rows we hold for India: in Mumbai two of the four most used sources were agency directories, and in Delhi a global directory sat level with the local sites. Bangalore produced neither. Worth noting too that the two cities elsewhere in the dataset where the leading source names the city in its domain, localseo.nyc in New York and mainchester.co.uk in Manchester, have no equivalent here. onecity.co.in and aiseo.solutions say nothing about Bangalore in their names, so whatever made them the answer was written on the page.

The counts are small and this page will be honest about their edges. They come from buyer side questions in the category we trade in, so what they show is the shape of a Bangalore answer and not the health of any particular industry here. ChatGPT and Gemini were the two engines read. Claude, Perplexity and Google AI Overviews sat out this run completely, which leaves them with no number on this page at all. These 12 readings are a slice of something larger. The three market run they came out of, 597 usable readings, is published whole with its prompts and its method at what AI assistants actually cite across 597 readings.

One city, two strings, and both have to resolve to you

Karnataka renamed the city with effect from 1 November 2014, so Bengaluru is the legal name and Bangalore is still what most buyers type and most older sources carry. That split runs straight through your own records. Your GST registration address, your registered office in the company filings and your Karnataka state registrations almost certainly say Bengaluru. Your early press coverage, your directory entries, your customers' purchase orders and the search box all say Bangalore.

An engine joining up mentions of a business is matching strings across sources, so this is a real cost rather than a trivia point. Three rules keep it cheap:

• Pick one spelling as primary and use it in every structured place. The contact page address, the footer, the maps listing and your schema address should all read the same way. Bengaluru is the safer primary, because it matches the registries a checker will look at.
• Let the other spelling appear once, naturally, in prose. One sentence that mentions both, for instance in the line where you say the office is in Bengaluru and has been in Bangalore since the year you started, connects the two strings without keyword stuffing.
• Never build two sets of pages for the two spellings. That splits your own signal in half and gives a model two thin pages instead of one solid one.

Then add the layer buyers actually type, which is neither spelling. People search Whitefield, Electronic City, Koramangala, HSR Layout, Indiranagar, Hebbal and the Outer Ring Road. Those names are more precise than the city itself and far less contested.

Why two small domains could each reach 4 of 12

The wider run explains how a small publisher gets to that number, and the explanation is that the two engines behave nothing alike.

Engine	Readings in the 15 October 2026 run	Readings citing that engine's single most cited domain
ChatGPT	306	176
Gemini	291	19

ChatGPT concentrates hard on a short list of documentation it trusts. Gemini spreads almost flat across many small sites, with its most cited domain appearing in only 19 of 291 readings. For a Bangalore business those are two separate jobs. On ChatGPT you are working against an entrenched set, which is slow. On Gemini there is no entrenched set to displace, and a page with real specifics on it is reachable now. Two small sites holding 4 readings each in this city is what the second job looks like when somebody has already done it.

The two Bangalore businesses, and they need different pages

This city contains both halves of the Indian market in unusual proportions. One half sells to people who are physically here: clinics, builders, schools, restaurants, repair services, brokers. For them the citable facts are the locality list, the hours, the rupee price basis and the licence numbers, and the local pack and consultant routes for that work are covered in buying search work in Bangalore as a local business.

The other half, which is the half this city is known for, sells to buyers who are nowhere near it. A software firm in Koramangala, a capability centre on the Outer Ring Road and a hardware startup in Electronic City are all asked category questions by people in other cities and other countries. For them the city is a credential rather than the query, and the page that gets cited needs the facts a distant buyer asks for:

• The named technology, standard or process instead of a claim about expertise. A buyer asks for the thing, not the adjective.
• Support hours in IST with the overlap window stated in the buyer's own terms, because that is the first objection a foreign client raises.
• Pricing that works in two currencies, with what is included and the GST position for an Indian buyer stated separately.
• The DPIIT recognition number if you hold one, which is checkable on the government's startup register, along with GSTIN and your Karnataka Shops and Commercial Establishments registration.
• A named person who answers, with a role. Answers that name businesses tend to come from pages that name people.

What Bengaluru founders ask after reading this row

Bangalore or Bengaluru on the website?

Bengaluru in every structured field, because that is what the registries hold, and one natural mention of Bangalore in the text so the older spelling still leads to you. Consistency across your own properties matters more than which one you choose.

Our customers are in the United States. Is a city page worth writing?

Write the category page first, since that is the question your buyers actually ask. The city page then does a narrower job: it holds your address, your registrations, your support hours and your named contacts in one place that other sources can be checked against.

Should we buy a listing on the four sites that got cited?

They are publishers rather than directories, so there is nothing to buy. The useful move is to read what they published and notice how specific it is, then publish something more specific about your own work with a date on it.

Which engines and how many readings is this?

A two engine figure. ChatGPT and Gemini gave us 12 readings here and 120 across the ten cities on 15 and 16 October 2026, with no citation to our domain anywhere in the set. The other three surfaces we cover are not in this dataset.

Do you have a Mysuru or Hubballi number?

No, and producing one would take about a minute and be worthless. Twelve readings exist for this city and not one for the rest of Karnataka, so a Mysuru or Hubballi claim would be invention. Those places are covered by the India market reading instead, which our India cut publishes with its denominator attached.

What four small sites prove about this city

The interesting thing about the Bangalore result is not that we were absent from it. It is that a city with more software engineering per square kilometre than anywhere else in the country had its buying answers written by four small publishers, and the thing they did was publish specifics while larger firms published positioning. Ask Gemini your buyer's question with Whitefield or Electronic City in it and read which pages it leans on. A disclosure, since this page argues for publishing specifics: a subscription to AI Knows Us is how a company buys this measurement across five assistants instead of running it by hand every month, and we take the writing work it uncovers as well. That is the interest behind the argument, and the four domains above are the evidence for it either way.