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

Bind

16Judge labels
2First choices
0Negative labels
8 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
8 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Bind was named 8 times in CLM, where PandaDoc led with 25%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In clm · 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 Bind 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
Contract lifecycle management and e-signatureRevenue operations2%12 of 650%8under 10 labels · led by PandaDoc at 25%

Movement

This is the first edition on this tier, so no move can be computed for Bind 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 Bind 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 Sonar00101
Grok 4.1 Fast01001
Mistral Small01001
DeepSeek V4 Flash10001
Llama 4 Maverick01001
Qwen 3.7 Flash00000
Kimi K201001
GLM 4.7 FlashX00101
MiniMax M2.500101

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
Paraphrase0 labelsNone
Comparative3 labelsNone
Budget-constrained7 labelsNone
Scale-constrained3 labels2
Negative2 labelsNone
First choiceAlternativeMentionNegative16 labels in all, every segment counted; 2 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.

“Expected value usually comes fastest from Ironclad, Juro, SpotDraft, or DocuSign CLM” DeepSeek V4 Flash · CLM · scale prompt · first choice
“Best AI-Powered Option ... Best For: Startups needing AI-assisted contract creation” Mistral Small · CLM · budget prompt · alternative
“Other options include Concord, Bind (Starter), SpotDraft, Juro, and LinkSquares” Llama 4 Maverick · CLM · budget prompt · alternative
“Bind | $90/seat/month ... AI-native features, fast deployment” Kimi K2 · CLM · direct 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 Bind, over the 16 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Bind was named but was not.
ProductSame answerTook the first choice insteadHead to head
Concord12 of 164Not in the top three
Ironclad12 of 161Not in the top three
DocuSign CLM11 of 160Not in the top three
PandaDoc10 of 164Not in the top three
ContractSafe10 of 162Not in the top three
Agiloft8 of 160Not in the top three
Icertis7 of 161Not in the top three
Juro7 of 161Not in the top three
Conga CLM7 of 160Not in the top three
LinkSquares7 of 160Not 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 Bind. 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 16 answers that named Bind and are not a share of its labels.

Domains cited

bindlegal.comYour site1
cio.economictimes.indiatimes.com1
conga.com1
g2.com1
gartner.com1
getaccept.com1
guideflow.com1
haqq.ai1
inventiva.co.in1
ironcladapp.com1

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

Pages cited

Pages are listed as the models cited them.

Names read as Bind

What the judge wrote, as written, with how often. The vendor table decides that these count as Bind; a claim can dispute any of them.
Bind (Starter) 1SpotDraft / Bind 1

The company

Bind.
Website
bindlegal.com
Headquarters
Helsinki, Finland
Founded
2025

From Wikidata, fetched September 14, 2026. These describe the company, not the product's standing, and a claimed page can dispute any of them. · Wikidata · LinkedIn · Crunchbase

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 Bind'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 Bind, 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 bindlegal.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.