Small business▼−4Since September 2026: 4% → 0%, −4 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 4% in September 2026
Inside the floor by 9 pointsRank 6 → 23 of 108
Fetcher
38Judge labels
1First choices
3Negative labels
13 / 14Models named it
2Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.
Best standing
0% in Recruiting CRM for mid-market buyers
Rank 23 of 111 in the mid-market standing
0 of 14 models made it the first choice on the direct prompt; 0% of its 8 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business24 of 114
0%8 labels
under the label threshold
Mid-market23 of 111
0%8 labels
under the label threshold
Enterprise46 of 78
0%1 label
under the label threshold
In recruiting crm · 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 Fetcher 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 |
|---|---|---|---|---|---|---|---|
| Recruiting CRM and sourcing | Talent acquisition | 0% | 23 of 111 | 0% | 8 | under 10 labels · led by Gem at 15% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| AI recruiting assistants | Talent acquisition | 0% | 30 of 90 | 0% | 3 | under 10 labels · led by Manatal at 31% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Fetcher held within the floor in all two categories: no change cleared 13 points.
Best category · Recruiting CRM · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 21 → 22 of 108
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 69 → 41 of 72
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Recruiting CRM | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 21 → 23 of 111 |
| AI recruiting assistants | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 23 → 30 of 90 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 13 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Fetcher across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Fetcher across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 3 | 0 | 3 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.1 Fast | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 0 | 1 | 1 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 1 | 1 | 0 | 2 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 1 | 0 | 1 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |
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
Direct8 labelsNone
Paraphrase0 labelsNone
Comparative17 labelsNone
Budget-constrained4 labels1
Scale-constrained5 labelsNone
Negative4 labelsNone
First choiceAlternativeMentionNegative38 labels in all, every segment counted; 1 of the 1 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.
“Best value-oriented options to evaluate: Fetcher, SeekOut, and Findem” Perplexity Sonar · Recruiting CRM · direct prompt · alternative
“Tools like Juicebox, Fetcher, or Leonar offer free tiers to test functionality” Kimi K2 · Recruiting CRM · negative prompt · alternative
“Fetcher/Pin for end-to-end autonomous sourcing and engagement” Muse Glimmer 30B · Recruiting CRM · comparative prompt · alternative
“For outbound automation: hireEZ or Fetcher.” Mistral Small · Recruiting CRM · 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 Fetcher, over the 38 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Fetcher was named but was not.ShowHide
Named alongside
The products named in the same answers as Fetcher, over the 38 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Fetcher was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
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 Fetcher. 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 38 of the 38 answers that named Fetcher and are not a share of its labels.
Domains cited
pin.com25
lever.co20
hiretruffle.com14
juicebox.ai14
selectsoftwarereviews.com14
herohunt.ai10
intervue.io10
leonar.app10
metaview.ai9
sapia.ai9
No domain is on file for Fetcher, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Fetcher stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Fetcher stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 2,900 a month (“fetcher”)
- AI search demand for its name, est.
- 113 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
0%
rank 23 of 111 in Recruiting CRM
Segment leader
15%
Gem
- First choices
- 1 across its categories
- Named in
- 38 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Follow Fetcher
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 Fetcher'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 Fetcher, 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 Fetcher by email, built from the raw record of the edition. It shows:
- where Fetcher 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 Fetcher, 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.