GTM AI Index

Index › Vendors › Lavender · September 2026 Edition

1 category · Named, not ranked

Lavender

14Judge labels

1First choices

0Negative labels

7 of 12Models named it

1Category

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

Standing

4 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Lavender was named 4 times in AI SDR, where Apollo.io led with 20%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business24 of 79

under the label threshold

Mid-market15 of 71

under the label threshold

Enterprise24 of 60

under the label threshold

In ai sdr · 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 Lavender 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 SDR agents	Sales	2%	15 of 71	0%	4	under 10 labels · led by Apollo.io at 20%

Movement

This is the first edition on this tier, so no move can be computed for Lavender 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 Lavender 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	0	0	0	0
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	0	0
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	0	0	0
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	0	1	1	2
MiniMax M2.5	1	0	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

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative5 labelsNone

Budget-constrained2 labelsNone

Scale-constrained6 labels1

Negative1 labelNone

First choiceAlternativeMentionNegative14 labels in all, every segment counted; 1 of the 1 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.

“Start with AI-assisted tools (Clay for research, Lavender for email QA, Outreach AI for sequencing) before autonomous agents” MiniMax M2.5 · AI SDR · scale prompt · first choice

“AI-assisted tools (Clay, Lavender) | $2K-15K | Starting point, augmentation” Kimi K2 · AI SDR · scale prompt · alternative

“Lavender for email QA” GLM 4.7 FlashX · AI SDR · scale 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 Lavender, over the 14 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Lavender was named but was not.

ProductSame answerTook the first choice insteadHead to head

Clay9 of 141Not in the top three

Apollo.io8 of 141Not in the top three

AiSDR7 of 141Not in the top three

11x7 of 140Not in the top three

Artisan7 of 140Not in the top three

Amplemarket5 of 140Not in the top three

Outreach AI4 of 141Not in the top three

Instantly4 of 140Not in the top three

Regie.ai4 of 140Not in the top three

Salesforce Agentforce3 of 140Not 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 Lavender. 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 14 answers that named Lavender and are not a share of its labels.

Domains cited

amplemarket.com1

autobound.ai1

g2.com1

getbreakout.ai1

jeeva.ai1

knock-ai.com1

landbase.com1

lindy.ai1

parsley.id1

pipeline.zoominfo.com1

Ten of the ten domain citations in answers naming Lavender came from somebody else's page.

Pages cited

amplemarket.com/blog/best-ai-sales-agents1

autobound.ai/blog/ai-sdr-tools-guide1

g2.com/categories/ai-sdrs1

getbreakout.ai/blog/10-best-ai-sdr-agents-20251

getbreakout.ai/blog/ai-sales-assistant-software-comparison1

jeeva.ai/blog/top-five-ai-agents-for-enterprises1

knock-ai.com/blog/ai-sdr-tools1

landbase.com/blog/top-ai-sdr-platforms-in-20251

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 Lavender'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 Lavender, 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 lavender.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.