GTM AI Index
Index Vendors › Bïrch · September 2026 Edition
1 category · Named, not ranked

Bïrch

5Judge labels
0First choices
0Negative labels
4 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
3 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Bïrch was named 3 times in Paid ads, where Google Ads led with 10%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In paid ads · 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 Bïrch 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.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Search and social advertising platformsMarketing0%37 of 1370%3under 10 labels · led by Google Ads at 10%

Movement

This is the first edition on this tier, so no move can be computed for Bïrch 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 Bïrch across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar01001
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash01001
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K201001
GLM 4.7 FlashX00000
MiniMax M2.500000

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
Paraphrase1 labelNone
Comparative3 labelsNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative5 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.

“Choose AdEspresso, Bïrch, or Ryze AI if your work is more paid social or truly cross-channel.” Perplexity Sonar · Paid ads · comparative prompt · alternative
“at ~$49–99/month is excellent for rule-based automation across Meta, TikTok, and LinkedIn” DeepSeek V4 Flash · Paid ads · paraphrase prompt · alternative
“Agencies wanting rules-based automation | Custom rules across Meta/Google/TikTok” Kimi K2 · Paid ads · comparative 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 Bïrch, over the 5 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Bïrch was named but was not.
ProductSame answerTook the first choice insteadHead to head
Optmyzr5 of 51Not in the top three
Skai4 of 52Not in the top three
Marin Software4 of 51Not in the top three
AdEspresso3 of 50Not in the top three
Opteo3 of 50Not in the top three
Ryze AI3 of 50Not in the top three
WordStream3 of 50Not in the top three
Adzooma2 of 51Not in the top three
Smartly2 of 51Not in the top three
Adalysis2 of 50Not 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 Bïrch. 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 5 answers that named Bïrch and are not a share of its labels.

Names read as Bïrch

What the judge wrote, as written, with how often. The vendor table decides that these count as Bïrch; a claim can dispute any of them.
Bïrch (formerly Revealbot) 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 Bïrch'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 Bïrch, 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 brch.io 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.