GTM AI Index

Index › Vendors › SiftHub · September 2026 Edition

1 category · Named, not ranked

SiftHub

8Judge labels

0First choices

1Negative labels

3 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. SiftHub was named 3 times in Proposals, where PandaDoc led with 49%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business35 of 61

under the label threshold

Mid-market24 of 79

under the label threshold

Enterprise28 of 76

under the label threshold

In proposals · 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 SiftHub 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
Proposal software	Revenue operations	0%	24 of 79	0%	3	under 10 labels · led by PandaDoc at 49%

Movement

This is the first edition on this tier, so no move can be computed for SiftHub 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 SiftHub 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	1	1
GPT-5.4 mini	0	0	0
Gemini 3.5 Flash	2	0	2
Perplexity Sonar	0	0	0
Grok 4.1 Fast	0	0	0
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	0	0	0
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

Direct3 labelsNone

Paraphrase0 labelsNone

Comparative3 labelsNone

Budget-constrained0 labelsNone

Scale-constrained2 labelsNone

Negative0 labelsNone

First choiceAlternativeMentionNegative8 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.

“SiftHub: An AI-native platform that automatically syncs with your live company knowledge bases” Gemini 3.5 Flash · Proposals · comparative prompt · alternative

“AI-first options like SiftHub use centralized knowledge repositories” Gemini 3.5 Flash · Proposals · 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 SiftHub, over the 8 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and SiftHub was named but was not.

ProductSame answerTook the first choice insteadHead to head

PandaDoc7 of 84Not in the top three

Loopio7 of 80Not in the top three

Responsive5 of 82Not in the top three

Qwilr5 of 80Not in the top three

Better Proposals4 of 80Not in the top three

Proposify4 of 80Not in the top three

AutogenAI3 of 82Not in the top three

GetAccept3 of 80Not in the top three

DealHub2 of 81Not in the top three

Qvidian2 of 81Not 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 SiftHub. 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 3 of the 8 answers that named SiftHub and are not a share of its labels.

Domains cited

oneflow.com3

sifthub.ioYour site3

autogenai.com2

flowcase.com2

getaccept.com2

guideflow.com2

lindy.ai2

lotuspetal.ai2

blog.quotewerks.com1

en.wikipedia.org1

Seventeen of the twenty domain citations in answers naming SiftHub came from somebody else's page.

Pages cited

oneflow.com/blog/best-proposal-management-software3

sifthub.io/blog/best-proposal-software-tools3

autogenai.com/blog/best-proposal-management-software2

getaccept.com/blog/proposal-software2

guideflow.com/blog/best-proposal-software2

lindy.ai/blog/proposal-software2

lotuspetal.ai/blog/2026/04/02/best-rfp-proposal-software-of-20262

autogenai.com/blog/best-ai-proposal-software-20261

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 SiftHub'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 SiftHub, 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 sifthub.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.