HR AI Index

Index › Vendors › Noon · September 2026 Edition

2 categories · Named, not ranked

Noon

19Judge labels

1First choices

3Negative labels

9 of 12Models named it

2Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.

Standing

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Noon was named 2 times in AI recruiting assistants and 1 other category, where GoPerfect led with 19%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business28 of 70

under the label threshold

Mid-market36 of 84

under the label threshold

Enterprise45 of 53

under the label threshold

In ai recruiting assistants · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Noon 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
AI recruiting assistants	Talent acquisition	0%	36 of 84	0%	1	under 10 labels · led by GoPerfect at 19%
Recruiting CRM and sourcing	Talent acquisition	0%	66 of 88	0%	1	under 10 labels · led by Lever at 20%

Movement

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

By model

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

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

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

Direct2 labelsNone

Paraphrase0 labelsNone

Comparative4 labels1not counted in share

Budget-constrained7 labelsNone

Scale-constrained6 labels1

Negative0 labelsNone

First choiceAlternativeMentionNegative19 labels in all, every segment counted; 1 of the 2 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.

“Runs the entire hiring funnel end-to-end—sourcing, evaluation, outreach, and scheduling—without human intervention.” Mistral Small · AI recruiting assistants · comparative prompt · first choice

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 Noon, over the 19 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Noon was named but was not.

ProductSame answerTook the first choice insteadHead to head

Manatal7 of 191Not in the top three

SeekOut7 of 191Not in the top three

LinkedIn Recruiter6 of 190Not in the top three

hireEZ6 of 190Not in the top three

Pin5 of 192Not in the top three

Gem5 of 190Not in the top three

GoodTime4 of 192Not in the top three

Calendly4 of 190Not in the top three

Eightfold AI4 of 190Not in the top three

Fetcher4 of 190Not in the top three

A head-to-head page exists where both products are in a category's top three. 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 Noon. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 18 of the 19 answers that named Noon and are not a share of its labels.

Domains cited

noon.aiYour site16

selectsoftwarereviews.com11

pin.com8

hiretruffle.com7

bestrecruitingtools.com5

aws.amazon.com4

candidate.fyi4

fluentbooking.com4

goodtime.io4

lever.co4

Fifty-one of the sixty-seven domain citations in answers naming Noon came from somebody else's page.

Pages cited

noon.ai/enterprise6

noon.ai/blog/articles/178-enterprise-ai-recruiting-software5

selectsoftwarereviews.com/buyer-guide/best-candidate-sourcing-tools5

aws.amazon.com/marketplace/pp/prodview-qfgvafapeqe6g4

bestrecruitingtools.com/blog/best-interview-scheduling-software-enterprise-20264

candidate.fyi/interview-scheduling-software4

fluentbooking.com/articles/best-interview-scheduling-software4

goodtime.io/pricing/hire4

Pages are listed as the models cited 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 Noon'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 Noon, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at noon.ai is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.