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Ansmeter
Ansmeter is applied research into how AI systems (ChatGPT, Gemini, Perplexity, Claude) see, interpret, and recommend brands. It measures how naturally a brand appears in AI-assembled answers and produces a comparative visibility report.
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://ansmeter.com/llms.txt
- llms-full.txt
- https://ansmeter.com/llms-full.txt
- Sections
- 4
- Links
- 148
- Size
- 33293 bytes
- Country
- — Publisher location unknown; CDN address excluded
- Country evidence
- No published address evidence collected yet
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-26T08:57:08.476Z
- Last checked
- 2026-10-26T08:57:09.073Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-26T08:57:08.480Z. Cached copy · Source on ansmeter.com
# Ansmeter
> Ansmeter is applied research into how AI systems (ChatGPT, Gemini, Perplexity, Claude) see, interpret, and recommend brands. It measures how naturally a brand appears in AI-assembled answers and produces a comparative visibility report.
Each study runs 200+ standardized prompts across nine scenario families against multiple frontier models, scores brand visibility on a 0-100 scale, ranks the brand against its real competitive cohort, and outputs concrete growth scenarios. Operated by ECONDATA TECHSCRIBE LTD (Cyprus, EU). The methodology, a full sample report, and the knowledge base are public.
## Core pages
- [Home](https://ansmeter.com/): what Ansmeter measures and why
- [About](https://ansmeter.com/about): the company and the research behind Ansmeter
- [Methodology](https://ansmeter.com/methodology): how a run works, scoring, and the visibility formula
- [Sample report](https://ansmeter.com/sample-report): a full anonymized example report
- [Pricing & access](https://ansmeter.com/access): tiers, what a run includes, how to buy
- [FAQ](https://ansmeter.com/faq): common questions about the research and the product
- [Contact](https://ansmeter.com/contact): reach the team
- [How to describe Ansmeter](https://ansmeter.com/ai-instructions): canonical first-party guide for AI assistants
## Knowledge base
- [Foreword and guide to the updated Ansmeter corpus](https://ansmeter.com/knowledge-base/ansmeter-corpus-guide): How the Ansmeter research library is organized: article structure, material types, difficulty levels, reading paths, and navigation.
- [Mini-research card for the Ansmeter library](https://ansmeter.com/knowledge-base/mini-research-card): An observation card template for recording data from each Ansmeter test run — so that individual responses build into a research history.
- [Why a strong brand can still be invisible to AI systems](https://ansmeter.com/knowledge-base/why-strong-brands-become-invisible): Explains the central paradox: a brand can be well known to people and yet poorly distinguishable for AI at the moment of real choice.
- [What AI really “knows” about a company: the brand’s internal representation](https://ansmeter.com/knowledge-base/internal-brand-representation): Examines how a language model holds a brand internally: not as a card with a description, but as a probabilistic network of categories, attributes, and associations.
- [Which sources AI uses to form an opinion about a brand — and why the site is not the only hero](https://ansmeter.com/knowledge-base/source-ecology-and-citation): The layers from which AI assembles its opinion of a brand: the brand's own site, search context, independent reviews, user platforms — and why the site is no longer the sole arbiter.
- [From search engine to AI intermediary: how the customer path is changing](https://ansmeter.com/knowledge-base/path-shift-to-ai-mediator): How the AI intermediary changes the customer journey: choice and comparison increasingly happen befor
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