GTM AI Index

Index › Vendors › Autobound · September 2026 Edition

3 categories · Named, not ranked

Autobound

6Judge labels

0First choices

1Negative labels

4 of 12Models named it

3Categories

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

Standing

3 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Autobound was named 3 times in Intent data and 2 other categories, where Bombora led with 35%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market34 of 62

under the label threshold

Enterprisenot named

No model named it for this buyer.

In intent data · 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 Autobound 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
B2B intent data providers	Sales	0%	34 of 62	0%	1	under 10 labels · led by Bombora at 35%
Experimentation and personalization	Marketing	0%	31 of 69	0%	1	under 10 labels · led by VWO at 34%
Proposal software	Revenue operations	0%	31 of 79	0%	1	under 10 labels · led by PandaDoc at 49%

Movement

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

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

Direct1 labelNone

Paraphrase2 labelsNone

Comparative1 labelNone

Budget-constrained0 labelsNone

Scale-constrained1 labelNone

Negative1 labelNone

First choiceAlternativeMentionNegative6 labels in all, every segment counted; 0 of the 0 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.

“Other notable options include Autobound and Quotewerks, which are also suitable for mid-market sales teams” Llama 4 Maverick · Proposals · paraphrase prompt · alternative

“Autobound specializes in B2B sales email personalization using 700+ real-time buying signals” Claude Haiku 4.5 · Experimentation · comparative prompt · alternative

“4. Autobound” Llama 4 Maverick · Intent data · paraphrase 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 Autobound, over the 6 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Autobound was named but was not.

ProductSame answerTook the first choice insteadHead to head

ZoomInfo2 of 62Not in the top three

Bombora2 of 61Not in the top three

6sense2 of 60Not in the top three

Apollo.io2 of 60Not in the top three

Cognism2 of 60Not in the top three

LinkedIn Sales Navigator2 of 60Not in the top three

Dynamic Yield1 of 61Not in the top three

Optimizely CMS1 of 61Not in the top three

Proposify1 of 61Not in the top three

11x1 of 60Not 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 Autobound. 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 1 of the 6 answers that named Autobound and are not a share of its labels.

Domains cited

alexbirkett.com1

autobound.aiYour site1

gartner.com1

guideflow.com1

personizely.net1

uselayers.com1

Five of the six domain citations in answers naming Autobound came from somebody else's page.

Pages cited

alexbirkett.com/personalization-software1

autobound.ai/blog/top-12-personalization-engines-for-marketing-20261

gartner.com/reviews/market/personalization-engines1

guideflow.com/blog/best-personalization-software1

personizely.net/blog/enterprise-personalization-solutions1

uselayers.com/articles/best-ecommerce-personalization-software1

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 Autobound'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 Autobound, 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 autobound.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.