GTM AI Index

Index › Vendors › Helply · September 2026 Edition

5 categories · Named, not ranked

Helply

35Judge labels

9First choices

2Negative labels

11 of 12Models named it

5Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-09-16.3, every buyer segment counted.

Standing

8 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Helply was named 8 times in Support chatbots and 4 other categories, where Intercom led with 35%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business6 of 84

under the label threshold

Mid-market15 of 106

under the label threshold

Enterprise30 of 122

under the label threshold

In support chatbots · 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 Helply 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
Chatbots and conversational AI for support	Customer	2%	15 of 106	0%	1	under 10 labels · led by Intercom at 35%
In-app messaging and customer communications	Customer	2%	17 of 113	0%	2	under 10 labels · led by Intercom at 20%
Knowledge base and self-service	Customer	2%	16 of 96	0%	1	under 10 labels · led by Notion at 18%
Customer feedback and surveys	Customer	2%	18 of 104	0%	2	under 10 labels · led by Google Forms at 15%
Customer support and helpdesk	Customer	0%	25 of 59	0%	2	under 10 labels · led by Freshdesk at 44%

Movement

This is the first edition on this tier, so no move can be computed for Helply yet. The next is due October 1, 2026. 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 Helply across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	1	0	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	1	0	1
Mistral Small	2	0	0	2
DeepSeek V4 Flash	0	0	0	0
Llama 4 Maverick	0	0	1	1
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	1	0	0	1
MiniMax M2.5	0	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

Direct18 labels5

Paraphrase13 labels3

Comparative0 labelsNone

Budget-constrained4 labels1

Scale-constrained0 labelsNone

Negative0 labelsNone

First choiceAlternativeMentionNegative35 labels in all, every segment counted; 9 of the 9 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 most mid-market B2B companies, Helply offers the most compelling value proposition with its pay-per-outcome model and strong AI features” GLM 4.7 FlashX · Knowledge base · direct prompt · first choice

“Helply is excellent if you want support to drive revenue outcomes” Claude Haiku 4.5 · In-app messaging · paraphrase prompt · first choice

“1. Helply - *Best Overall for B2B Support Teams*” Mistral Small · Feedback · direct prompt · first choice

“I'd recommend Helply as your top choice” Mistral Small · Support chatbots · paraphrase 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 Helply, over the 35 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Helply was named but was not.

ProductSame answerTook the first choice insteadHead to head

Intercom12 of 352Not in the top three

Plain10 of 352Not in the top three

Zendesk9 of 353Not in the top three

Pylon9 of 351Not in the top three

Help Scout9 of 350Not in the top three

Freshdesk8 of 353Not in the top three

Notion7 of 353Not in the top three

Tidio6 of 352Not in the top three

Document3606 of 351Not in the top three

Confluence6 of 350Not 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 Helply. 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 9 of the 35 answers that named Helply and are not a share of its labels.

Domains cited

helply.comYour site9

learn.g2.com5

front.com4

chatbase.co3

thecxlead.com3

usepylon.com3

zendesk.com3

assembly.com2

featurebase.app2

g2.com2

Twenty-seven of the thirty-six domain citations in answers naming Helply came from somebody else's page.

Pages cited

helply.com/blog/customer-communication-tools4

chatbase.co/blog/customer-messaging-tools3

front.com/blog/customer-messaging-platform3

helply.com/blog/10-best-customer-messaging-tools-for-businesses3

assembly.com/blog/best-knowledge-base-software2

getvero.com/resources/top-6-sms-marketing-platforms-for-b2b-in-20262

helpjuice.com/blog/knowledge-base-software-buying-guide2

helply.com/blog/best-knowledge-base-software-platforms2

Pages are listed as the models cited them.

Names read as Helply

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

Groove / Helply 1

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 Helply'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 Helply, 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 helply.com 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.