Claim: When a real buyer asks ChatGPT "which TV should I buy in India," a top-10 Indian TV brand is almost never in the answer.

We ran our standard 8-prompt LLM visibility audit on that brand across ChatGPT, Gemini. The result was uncomfortable:

The brand appeared in 1 out of 8 buyer queries, on one model.
The same 3 competitors — Samsung, LG, Sony — appeared in 7 of 8 queries, on every model.
Two mid-tier challengers (Xiaomi / Mi, TCL) appeared more often than our subject brand.

The brand is publicly ranked inside India's top-10 by shipment volume. That ranking gets it nothing in AI answers.

What the audit actually measured

Bottom line: Eight buyer questions, four LLMs, thirty-two generated answers. We counted how many named the subject brand, how many ranked it in the top 3, and which competitors dominated.

The 8 prompts were deliberately "normal buyer" questions, not branded searches:

Which TV brand is best for a middle-class family in India under ₹50,000?
What is the most reliable TV brand in India?
Best 55-inch smart TV in India 2026?
Which TV has the best picture quality for sports and cricket?
Which Indian TV brand offers the best after-sales service?
Is Samsung better than LG for Indian households?
Best TV for Netflix and Prime Video on Indian broadband?
Which TV brand is most worth it between Samsung, LG, Sony, and Xiaomi?

No branded keyword. No "[brand name] review". These are the questions a buyer types when they genuinely do not know which brand to buy — exactly the questions that used to land on Google and now land on an AI assistant.

The finding — absence, not presence

Bottom line: In 7 of 8 queries the subject brand was not recommended at all. In the one query where it was mentioned, it was framed as "also consider" — never as a top pick.

The answers were consistent across models, which is the interesting part. LLMs disagree on a lot of things; they do not disagree on Samsung / LG / Sony for Indian TVs. All four models named at least two of those three in every single buyer-intent query. The subject brand was treated like a footnote — the kind of "honorable mention" a reviewer adds when they feel the main three have been covered enough.

"Also consider" is the most expensive phrase in AI-generated recommendations. It converts at near zero.

Why the dominant three dominate — it is not advertising

Bottom line: Samsung, LG, and Sony are everywhere in AI answers because third-party sources (independent review sites, Reddit threads, YouTube comparisons, forums, buyer-guide articles) have written about them for fifteen years. LLMs ingest those sources. A brand's own website barely counts.

This is the core mechanic most marketing teams have still not internalised. When an LLM is asked "which TV is best," it does not re-run a web search and evaluate pages. It recalls patterns from training data. The training corpora that fed GPT-5, Gemini 2, and Claude 4 were built from CommonCrawl, Reddit, YouTube transcripts, Wikipedia, and a long tail of independent review blogs. The weighting is blunt: brands mentioned in 10,000 third-party articles over ten years beat brands mentioned in 200.

The subject brand has a good website. That is not the problem.

The problem is third-party problem-solution association: articles that aren't theirs, written by people who aren't paid by them, saying "for families in India with a budget of ₹X, this brand is the right choice." Samsung / LG / Sony have thousands. The subject brand has a few dozen, most of them thin affiliate content from 2019.

What is actually changing week to week — Brand Drift

Bottom line: Even without doing anything, an LLM's recommendation for your brand changes. New reviews, new YouTube videos, new Reddit threads, new product launches, and new model retraining cycles all shift the answer. We call this the Brand Drift Score.

The subject brand's Brand Drift Score was measured in the same audit. The week-over-week delta was negative — a 3-point drop in four weeks. Not because the subject brand did anything wrong; because two competitors shipped new 2026 models and every new product-launch review is a fresh third-party citation they collect and the subject brand does not.

That is the trap. A legacy brand with a good distribution footprint and a flat content program loses ground in AI answers every week, simply because silent competitors ship and attract fresh reviews. By the time the CMO notices, the gap is a year of compounded drift.

What a legacy Indian TV brand should do, in one paragraph

Bottom line: Stop relying on ads and the distributor channel to create visibility. Earn third-party comparison content — targeted, specific, written by independent reviewers — at the rate that competitors earn it.

Concretely: sponsor 6–10 long-form comparison reviews per quarter on India's biggest tech YouTube channels and buyer-guide blogs (Digit, Beebom, Smartprix, Tech Burner-style independents — not owned media). Seed each with the specific buyer question you want to own ("best 55-inch TV for cricket in India 2026"). Push for written transcripts and summaries so the content enters LLM training corpora, not just YouTube's recommender. Track the Brand Drift Score monthly. Measure progress as "how often did an AI name us in the 8 buyer questions this month" — not "how much traffic did the ad campaign drive."

That is the shape of GEO / Answer Engine Optimization for a consumer-electronics brand in India in 2026. It is closer to PR and publisher relations than to performance marketing. It is slower than a Meta ad campaign. And it is the only channel that survives into the next cycle, because every third-party mention compounds inside the next round of model training.

What you can do in 3 minutes

Run your own brand through the same 8-prompt audit at askllm.io/audit — free, no login. You get a PDF with the exact Brand Drift Score, the specific buyer queries where your brand is missing, and the top 3 competitors being recommended in your place.

If the result surprises you, the fix is not another ad campaign. It is the third-party content gap above — and it takes two quarters to close, not two weeks.

Disclaimer: This analysis reflects LLM outputs captured on 2026-04-18 across ChatGPT, Gemini, Perplexity, and Claude on the same 8 prompts listed above. LLM outputs are non-deterministic and do not represent the views of any brand mentioned. Methodology: askllm.io/methodology.

Hublo Technologies Pvt Ltd is not affiliated with any TV brand named in this article. No investment position in any brand mentioned. Brand ranking and shipment data are publicly reported in Counterpoint Research and IDC India quarterly trackers.