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Home→Case studies→Telegram store builder, starting from zero

ChatGPT names the service first of eight

At the start, the site gave AI models nothing to go on: the service was described for human visitors only. I ran the full cycle – technical access for crawlers, pricing and feature facts in extractable form, pages built around direct answers and a machine-readable llms.txt file. ChatGPT’s answer now follows that file and uses its wording: catalog, order checkout, broadcasts, automation.

Before the work
How do I start an online store in Telegram?
None of the 4 AI systems I track named the service for this question. The site had no machine-readable description1, and pricing facts lived only in the page layout meant for people2.
  • llms.txt – missing
  • Extractable facts – missing
Now, ChatGPT
How do I start an online store in Telegram?
WebBot.shop1 – a specialized service for launching a store as a Telegram Mini App. Suitable for building a catalog, product cards, a cart, order checkout, broadcasts and sales automation inside Telegram2.
  • WebBot.shop – named first of eight
  • The service’s llms.txt – same words as in the answer
1st of 8 position among the services named
4 of 4 tracked AI systems name the service
90 days from start to first appearance in answers

The project is in the Russian-speaking market. The prompt and ChatGPT’s answer were in Russian; the answer is translated for this page, with the citation markers kept where the model placed them. No answers were saved before the work started, so the “before” panel describes the starting point in words.

Updated

What I did

  • Audited how 4 AI systems answer questions about starting a store in Telegram, and which services they name.
  • Fixed technical access so AI crawlers could reach and read the key pages.
  • Moved pricing and feature facts out of the visual layout into plain text a model can extract and quote.
  • Reworked the main pages and wrote new ones, each built around a direct answer to a buyer question.
  • Wrote llms.txt – a machine-readable description of what the service does and what it includes.
  • Tracked one fixed set of prompts in the same 4 AI systems to measure the change over 90 days.

ChatGPT reused the wording of the service’s own llms.txt and pages. The description you publish in plain, extractable form is the one a model is most likely to repeat.

Services used in this project

AI visibility audit

Where you appear across up to 7 AI systems, who gets recommended instead of you, and a 90-day action plan.

$490 →

Technical GEO

Crawler access, rendering, schema markup, llms.txt and machine-readable facts – found and fixed.

from $490 →

Page optimization for AI search

Existing pages reworked into extractable answers: structure, facts, FAQ blocks, markup.

from $90 per page →

Content for AI search

New pages written for the questions people ask AI systems about your market.

from $190 per article →

AI visibility monitoring

A fixed prompt set checked every month across AI systems, with a report I write and a plan for next month.

from $290/mo →

Monthly GEO plans

The same work as a monthly package: tracking, fixes, new pages and external mentions.

from $490/mo →

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