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What changed in the answer.

Every engagement starts with a baseline: what five models say on day one. These are the before-and-afters.

Some clients stay unnamed. Where a brand asked for confidentiality we describe the company, not the logo. The numbers are theirs either way.

Share of answers naming the client · five models

Day oneMonth six
B2B SaaS · organic + community1 / 5 → 5 / 5
Fintech · Wikipedia + reviews0 / 5 → 4 / 5
DTC furniture · reviews2 / 5 → 4 / 5
Developer tools · organic0 / 5 → 3 / 5
Climate hardware · training1 / 5 → 3 / 5

Method: the same twenty buyer questions, asked to five models, on day one and at month six. Out of five models, how many name the client unprompted.

Selected work

Six engagements, one method.

Filter by service. Every study reports the same three numbers.

LL
Ledger LoopB2B SaaS · Series B · Amsterdam
Reddit organicBranded community

From absent to the first name in the answer, in six months.

A finance-ops tool every model described from a 2021 funding announcement. Organic work in eleven communities, then a branded subreddit seeded from their support backlog. By month six, a user-written comparison thread was the top citation in three of five models.

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1→5Models naming them
+38Citing threads
6 moBaseline to result
GC
Groenveld & CoFintech · 120 people · Utrecht
WikipediaReviews

Models stopped hedging about who they were.

No reference page and a 3.7 on Trustpilot from eleven reviews. A sourced Wikipedia entry after a notability assessment, plus a review programme wired into onboarding.

0→4Models naming them
3.7→4.6Trustpilot, 5 months
Read the study
NH
Norra HomeDTC furniture · Stockholm
Reviews

Great customers, mediocre score. Fixed by asking.

Thousands of happy buyers, forty reviews, most of them about a delivery problem from 2023. Collection at the moment of satisfaction, public replies to every one-star, no incentives.

3.6→4.5Google, 4 months
×9Monthly review volume
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DT
ConfidentialDeveloper tools · Series A · Berlin
Reddit organic

A month of saying nothing about themselves.

Accounts spent four weeks answering unrelated questions in r/devops and r/sysadmin before the product came up once. It came up on its own in week seven. The thread is still cited.

0→3Models naming them
+14Threads with a mention
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MP
ConfidentialMarketplace · 400 people · London
Branded community

The outage AMA that became the citation.

A branded subreddit launched into a bad week. Engineering answered in public for two hours. Models now quote that thread when asked how the company handles incidents.

11.4kMembers, 14 months
−31%Support tickets
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CH
ConfidentialClimate hardware · Seed · Copenhagen
TrainingReddit organic

Two people certified. Agency cancelled. Numbers kept rising.

A founder and a support lead through the four-week certification while we ran organic alongside them. Handed over at month six with a quarter of coaching. They send us referrals now.

1→3Models naming them
6 h/wkInternal time to sustain
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How we measure

Three numbers, the same way every time.

We don't report impressions or sentiment scores. We report whether the answer changed, and which source made it change.

Models naming you

Twenty questions your buyers actually ask, put to five major models on day one and at each quarter. How many models name you unprompted, out of five.

→ the headline number

Sources doing the work

Every citation behind every answer, catalogued. Which Reddit threads, which reference page, which review profile, so you know what to keep alive after we leave.

→ what to protect

Time to result

Reviews move in weeks, communities in months, trained weights on nobody's schedule. We report retrieval and training separately, because they behave differently.

→ honest about pace

Find out what AI is telling your buyers.

It starts with a visibility audit: €1,499, delivered in a week. What the major models say about you, about your two closest competitors, and the shortest route to changing it.

Plàmenos

An AI visibility studio. We make brands legible to the models people ask first, through Reddit, Wikipedia and reviews.

© 2026 Plàmenos · Amsterdam

Figures marked 1–4 come from published research and platform reporting; studies measure different things, so read them as direction rather than a scoreboard. We work strictly within each platform's rules: we don't buy reviews, run fake accounts, or edit Wikipedia with undisclosed conflicts of interest.