GTM AI Index

Index › Vendors › Data Ladder · September 2026 Edition

1 category · Named, not ranked

Data Ladder

7Judge labels

0First choices

1Negative labels

6 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

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Data Ladder was named 2 times in CRM data quality, where Insycle led with 34%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business94 of 115

under the label threshold

Mid-market35 of 104

under the label threshold

Enterprise29 of 104

under the label threshold

In crm data quality · 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 Data Ladder 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
CRM data quality and deduplication	Revenue operations	0%	35 of 104	0%	2	under 10 labels · led by Insycle at 34%

Movement

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

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

Direct1 labelNone

Paraphrase1 labelNone

Comparative1 labelNone

Budget-constrained1 labelNone

Scale-constrained2 labelsNone

Negative1 labelNone

First choiceAlternativeMentionNegative7 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.

“Fuzzy matching algorithms for complex duplicates” GLM 4.7 FlashX · CRM data quality · 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 Data Ladder, over the 7 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Data Ladder was named but was not.

ProductSame answerTook the first choice insteadHead to head

Cloudingo4 of 71Not in the top three

Insycle4 of 71Not in the top three

DemandTools3 of 71Not in the top three

ZoomInfo Operations3 of 71Not in the top three

Dedupely3 of 70Not in the top three

Apollo.io2 of 71Not in the top three

Informatica Data Quality2 of 71Not in the top three

Cleanlist2 of 70Not in the top three

DataGroomr2 of 70Not in the top three

HubSpot Breeze Intelligence2 of 70Not 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.

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 Data Ladder'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 Data Ladder, 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 dataladder.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.