GTM AI Index

Index › Products › GovDash · October 2026 Edition

1 category · Named, not ranked

GovDash

9Judge labels

0First choices

1Negative labels

6 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10-05.2, every buyer segment counted.

Standing

6 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. GovDash was named 6 times in RFP response, where Loopio led with 25%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market20 of 82

under the label threshold

Enterprise38 of 54

under the label threshold

In rfp response · 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 GovDash 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	Since September 2026
RFP response software	Sales	0%	20 of 82	0%	6	under 10 labels · led by Loopio at 25%

Movement

This is the first edition on this tier, so no move can be computed for GovDash yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated GovDash across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	Alternative	Mention	Labels
Claude Haiku 4.5	1	0	1
GPT-5.4 mini	0	0	0
Gemini 3.5 Flash	2	0	2
Perplexity Sonar	1	0	1
Grok 4.20	0	1	1
Mistral Small	0	0	0
DeepSeek V4 Flash	0	0	0
Llama 4 Maverick	1	0	1
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
GPT-6 Luna	0	0	0
Muse Glimmer 30B	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

Paraphrase0 labelsNone

Comparative5 labelsNone

Budget-constrained0 labelsNone

Scale-constrained1 labelNone

Negative2 labelsNone

First choiceAlternativeMentionNegative9 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.

“Specializes in federal capture-to-contract workflows and long-form compliance narratives for US federal contractors (GovCon).” Claude Haiku 4.5 · RFP response · comparative prompt · alternative

“For this, you should look at GovCon-specific platforms like GovDash, Civio, or Procurement Sciences.” Gemini 3.5 Flash · RFP response · scale prompt · alternative

“GovCon-Specific Suites | GovDash, Civio AI, or Procurement Sciences” Gemini 3.5 Flash · RFP response · comparative prompt · alternative

“government-specific platforms built around FAR compliance, Section L/M parsing” Llama 4 Maverick · RFP response · negative 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 GovDash, over the 9 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and GovDash was named but was not.

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ProductSame answerTook the first choice insteadHead to head

Responsive9 of 96Not among the top eight

Loopio9 of 95Not among the top eight

Upland Qvidian6 of 90Not among the top eight

Arphie5 of 90Not among the top eight

AutoRFP.ai5 of 90Not among the top eight

PandaDoc4 of 90Not among the top eight

Procurement Sciences4 of 90Not among the top eight

Proposify4 of 90Not among the top eight

AutogenAI3 of 90Not among the top eight

Tribble3 of 90Not among the top eight

A head-to-head page exists where both products are among a category's top eight. 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 GovDash. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 9 of the 9 answers that named GovDash and are not a share of its labels.

Domains cited

civio.ai7

inventive.ai6

loopio.com6

1up.ai5

arphie.ai5

storyflow.so5

responsive.io4

autogenai.com3

autorfp.ai3

clarity-ventures.com3

Forty-seven of the forty-seven domain citations in answers naming GovDash came from somebody else's page.

Pages cited

civio.ai/insights/10-best-government-rfp-response-software-tools7

1up.ai/blog/the-best-rfp-software-buyers-guide5

arphie.ai/blog/top-5-rfp-tools-for-streamlining-your-proposal-process5

storyflow.so/blog/best-rfp-response-tools-20265

loopio.com/blog/best-response-management-software4

autogenai.com/blog/best-rfp-software-20263

autogenai.com/blog/the-best-rfp-software-in-20263

clarity-ventures.com/ecommerce-integrations/rfp-tools3

Pages are listed as the models cited them.

Follow GovDash

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

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 GovDash'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 GovDash, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for GovDash by email, built from the raw record of the edition. It shows:

where GovDash is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name GovDash, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.