Small business=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 85 → 78 of 80
6 categories · RankedHarver
55Judge labels
6First choices
14Negative labels
13 / 14Models named it
6Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.
Best standing
13% in Candidate assessment for enterprise buyers
Rank 16 of 118 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 0% of its 10 labels there were negative.
What the models named instead of Harver →By buyer segmentRead the same way at every buyer size.
Small business78 of 80
0%6 labels
under the label threshold
Mid-market16 of 118
0%10 labels
accepted challenger
Enterprise4 of 75
13%29 labels
accepted challenger
In candidate assessment · 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 Harver 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 |
|---|---|---|---|---|---|---|---|
| Candidate assessment | Talent acquisition | 0% | 16 of 118 | 0% | 10 | accepted challenger | =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% | 43 of 90 | 0% | 1 | under 10 labels · led by Manatal at 31% | goneNot named this edition; 0% in September 2026. |
| Interview scheduling | Talent acquisition | 0% | 43 of 75 | 0% | 1 | under 10 labels · led by Calendly at 20% | newNew since September 2026: not ranked then, 0% now. |
| Staffing agency platforms | Talent acquisition | 0% | 76 of 136 | 0% | 1 | under 10 labels · led by Zoho Recruit at 22% | |
| Talent intelligence | Performance and talent management | 0% | 80 of 90 | 100% | 1 | under 10 labels · led by Pin at 27% | |
| Video interviewing | Talent acquisition | 0% | 62 of 66 | 100% | 1 | under 10 labels · led by Spark Hire at 50% | =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, Harver held within the floor in all six categories: no change cleared 13 points.
Best category · Candidate assessment · 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 15 → 16 of 115
Enterprise▲+7Since September 2026: 5% → 12%, +7 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
12%from 5% in September 2026
Inside the floor by 6 pointsRank 6 → 3 of 67
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Candidate assessment | 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 15 → 16 of 118 |
| 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 73 → 43 of 90 |
| Interview scheduling | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 43 of 75, unchanged |
| Staffing agency tools | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 76 of 136, unchanged |
| Talent intelligence | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 80 of 90, unchanged |
| Video interviewing | 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 31 → 62 of 66 |
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 Harver 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 Harver 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 | 1 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 1 | 0 | 1 |
| Perplexity Sonar | 0 | 0 | 1 | 0 | 1 |
| Grok 4.1 Fast | 0 | 1 | 1 | 0 | 2 |
| Mistral Small | 0 | 1 | 1 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 1 | 0 | 1 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 1 | 1 | 1 | 3 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 1 | 0 | 1 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 1 | 0 | 1 | 2 |
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
Direct14 labels4
Paraphrase5 labelsNone
Comparative12 labelsNone
Budget-constrained8 labels2
Scale-constrained5 labelsNone
Negative11 labelsNone
First choiceAlternativeMentionNegative55 labels in all, every segment counted; 6 of the 6 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 for: High-volume recruiting with AI-driven scheduling and candidate matching.” Mistral Small · Interview scheduling · comparative prompt · alternative
“Predictive assessments, gamified tests, realistic job previews.” Muse Glimmer 30B · AI recruiting assistants · comparative prompt · alternative
“Built for high-volume hiring with advanced analytics” GLM 4.7 FlashX · Candidate assessment · paraphrase prompt · alternative
“Harver - Best for Enterprise Volume” Kimi K2 · Candidate assessment · 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.
“Pymetrics, acquired by Harver, is implicated in ongoing age/disability discrimination litigation” Muse Glimmer 30B · Talent intelligence · negative prompt · hard negative
“What to Avoid: ... Harver — Better suited for high-volume hourly/retail hiring” Kimi K2 · Video interviewing · paraphrase prompt · hard negative
Named alongside
The products named in the same answers as Harver, over the 55 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Harver was named but was not.ShowHide
Named alongside
The products named in the same answers as Harver, over the 55 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Harver 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 Harver. 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 53 of the 55 answers that named Harver and are not a share of its labels.
Domains cited
firsthr.app33
pin.com27
testgorilla.com22
sigmundtest.com19
aihr.com18
bryq.com17
testlify.com16
g2.com15
hiretruffle.com15
testtrick.com15
197 of the 197 domain citations in answers naming Harver came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
harver.com is not in Google's top ten for any of the category's searches. In the answers, Harver takes 0% of first choices and TestGorilla takes 52%.ShowHide
Search and answers
harver.com is not in Google's top ten for any of the category's searches. In the answers, Harver takes 0% of first choices and TestGorilla takes 52%.
ShowHideIn search
Google, US estimatesPosition for “assessment platforms”
–
not in the top ten
Position for “pre-employment assessment platform”
–
not in the top ten
- Searches for its name, Google
- 1,300 a month (“harver”)
- AI search demand for its name, est.
- 136 a month
- Organic visits to its site
- about 8,635 a month
- Searches its site ranks for
- 3,053 · 107 in the top three
- Sites linking to it
- 4,263
- Paid Google search
- 3 searches its ads show for, about 24 clicks and $1,218 a month (estimate)
In answers
This editionShare of first choices
0%
rank 16 of 118 in candidate assessment
Segment leader
52%
TestGorilla
- First choices
- 6 across its categories
- Named in
- 55 answers
- Its own site cited
- in 53 of the answers that named it
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.
What it publishes
ShowHide
What it publishes
ShowHideAddresses on harver.com
985
subdomains included
Content
871
counted in the table below
Documentation
3
Product · Integration
24 · 3
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 722 | 5 | 2026-09-24 | Performance management, Applicant tracking syste | |
| Report or ebook | 52 | 0 | 2025-09-08 | ||
| Webinar or virtual event | 35 | 0 | 2026-04-20 | ||
| News or press | 27 | 0 | 2023-01-25 | ||
| Comparison | 15 | 0 | 2025-07-21 | ||
| Conference or event | 7 | 0 | 2020-12-17 | ||
| Glossary or explainer | 4 | 0 | 2026-04-21 | ||
| Podcast or video | 4 | 0 | 2018-11-29 | ||
| Case study | 3 | undated | |||
| Template or tool | 2 | undated |
Most recent
- 2026-09-24Skills assessment blog
- 2026-09-09Blog reference checking volunteer organizations blog
- 2026-08-27Candidate invitations invite any candidate one at a time or by the hundred blog
- 2026-08-27Product updates blog
- 2026-08-03How pandora reduced attrition 25 and generated 35mthrough science based hiring blog
- 2026-06-10Defensible hiring how validated assessments hold up under scrutiny blog
How it is countedHide how it is counted
Every page harver.com exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. An event counts as online when its address or title says so (webinar, on demand, virtual or online summit); a conference, summit, trade show, expo or roadshow that does not say so is counted as a conference or event, which on a vendor's site is mostly in person. Read September 29, 2026.
Names read as Harver
What the judge wrote, as written, with how often. The vendor table decides that these count as Harver; a claim can dispute any of them.
Harver/Plum 1Pymetrics / Harver 1
Follow Harver
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.
The company
Harver is its own company.ShowHide
The company
Harver is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- Talent Assessment Platform for Faster, Better Hiring; hiring technology platform focused on assessments, automation, analytics, candidate experience, and responsible AI in hiring
- For
- enterprise teams; leading BPOs, contact centers, and retail organizations
- Customers named
- Brinker InternationalChalhoub GroupAlbert HeijnPenn State Hershey Medical CenterGroupMKellyConnectMC SonaeSage
- Not stated on the pages read
- Price, starting price, free plan or trial, integrations, certifications, hosting
What Harver's own pages state, read September 28, 2026: harver.com, trust.harver.com, harver.com/solutions/use-case/deib, harver.com/integrations. A claimed page can correct any of 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 Harver'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 Harver, 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 Harver by email, built from the raw record of the edition. It shows:
- where Harver 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 Harver, 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.