Every category where a model named Walor for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant	Since September 2026
Whistleblowing and investigations	Core HR and payroll	2%	13 of 107	0%	5	under 10 labels · led by FaceUp at 25%

Movement

This is the first edition on this tier, so no move can be computed for Walor yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated Walor across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	0	1	1	2
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	0	0	1	1
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	0	0	0
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	1	0	0	1
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	0	0	0	0
GPT-6 Luna	0	0	0	0
Muse Glimmer 30B	0	1	0	1

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct4 labels2

Paraphrase1 labelNone

Comparative3 labels1not counted in share

Budget-constrained8 labels1

Scale-constrained0 labelsNone

Negative0 labelsNone

First choiceAlternativeMentionNegative16 labels in all, every segment counted; 3 of the 4 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“For a limited budget, start with Walor. It offers the best middle ground” Qwen 3.7 Flash · Whistleblowing · budget prompt · first choice

“Walor is an intelligent, user-friendly whistleblowing platform designed specifically for small to mid-sized enterprises” Claude Haiku 4.5 · Whistleblowing · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Walor, over the 16 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Walor was named but was not.

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ProductSame answerTook the first choice insteadHead to head

FaceUp11 of 163Not among the top eight

Whistlelink10 of 166Not among the top eight

NAVEX One6 of 160Not among the top eight

SafeHotline5 of 163Not among the top eight

Whistleblower Software4 of 161Not among the top eight

Ethico4 of 160Not among the top eight

VoxWel3 of 161Not among the top eight

Ordio3 of 160Not among the top eight

Whispli3 of 160Not among the top eight

EthicsPoint2 of 161Not among the top eight

A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Walor. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 16 of the 16 answers that named Walor and are not a share of its labels.

Domains cited

faceup.com15

firsthr.app11

ordio.com10

navex.com9

pebb.io7

g2.com6

itechguides.com6

us.fitgap.com6

guideflow.com5

whistlelink.com5

Eighty of the eighty domain citations in answers naming Walor came from somebody else's page.

Pages cited

faceup.com/en/blog/best-whistleblowing-software-tools15

firsthr.app/compare/general/whistleblower-platform11

ordio.com/en/insights/guides/whistleblowing-software8

ordio.com/en/insights/ratgeber/whistleblowing-software7

pebb.io/articles/top-7-whistleblower-tools-in-20257

navex.com/en-us/platform/whistleblowing-software-solutions6

navex.com/en-us/small-business/ethicspoint5

guideflow.com/blog/whistleblowing-software4

Pages are listed as the models cited them.

Search and answers

walor.io is not in Google's top ten for any of the category's searches. In the answers, Walor takes 2% of first choices and FaceUp takes 25%.

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In search

Google, US estimates

Position for “whistleblowing and investigation platform”

–

not in the top ten

Position for “whistleblowing and investigation platforms”

–

not in the top ten

Searches for its name, Google: 170 a month (“walor”)
AI search demand for its name, est.: 20 a month
Organic visits to its site: about 374 a month
Searches its site ranks for: 117 · 2 in the top three
Sites linking to it: 125
Paid Google search: No ads found in the estimate; that does not mean it runs none

Ads on Google

None found in Google's ad transparency records for the US

In answers

This edition

Share of first choices

rank 13 of 107 in whistleblowing

Segment leader

25%

FaceUp

First choices: 3 across its categories
Named in: 16 answers
Its own site cited: in 16 of the answers that named it

Search figures are US estimates from DataForSEO, read October 5, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.

What it publishes

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Addresses on walor.io

135

subdomains included

Content

counted in the table below

Documentation

Product · Integration

0 · 0

Kind	Pages	Last 90 days
Glossary or explainer	52	undated
Blog	29	undated
Comparison	1	undated

How it is countedHide how it is counted

Every page walor.io exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Read October 5, 2026.

Names read as Walor

What the judge wrote, as written, with how often. The vendor table decides that these count as Walor; a claim can dispute any of them.

Walor/Whistlelink 1

Follow Walor

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

The company

Walor is its own company.

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In its own words

Stated by the vendor, not checked

Positioning: Whistleblowing made simple
walor.io
For: small and medium enterprises
walor.io
Price stated: Choose between two price tiers. No hidden fees or extra charges. walor.io
Starting price: 60 walor.io/pricing
Free plan or trial: Try it for free walor.io
Certifications: ISO37002ISO27001
Hosting: EU-based servers
Customers named: OLIOLICofocoLiberal Alliances Ungdom
Not stated on the pages read: Integrations

What Walor's own pages state, read October 5, 2026: walor.io, walor.io/pricing, walor.io/security. A claimed page can correct any of them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Walor's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Walor, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for Walor by email, built from the raw record of the edition. It shows:

where Walor is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name Walor, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.