Small business▲+7Since September 2026: 0% → 7%, +7 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
7%from 0% in September 2026
Inside the floor by 4 pointsRank 28 → 4 of 72
3 categories · RankedSybill
38Judge labels
8First choices
0Negative labels
11 / 14Models named it
3Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10-05.2, every buyer segment counted.
Best standing
7% in Revenue intel for small business buyers
Rank 5 of 76 in the mid-market standing
0 of 14 models made it the first choice on the direct prompt; 0% of its 6 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business4 of 75
7%17 labels
accepted challenger
Mid-market5 of 76
8%6 labels
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
In revenue intel · 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 Sybill 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 |
|---|---|---|---|---|---|---|---|
| Revenue intelligence and forecasting | Sales | 8% | 5 of 76 | 0% | 6 | under 10 labels · led by Clari Copilot at 28% | ▲+9Since September 2026: 0% → 9%, +9 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Conversational intelligence and call recording | Sales | 0% | 24 of 91 | 0% | 3 | under 10 labels · led by Avoma at 41% | newNew since September 2026: not ranked then, 0% now. |
| Sales engagement platforms | Sales | 0% | 43 of 66 | 0% | 1 | under 10 labels · led by Apollo.io at 35% | newNew since September 2026: not ranked then, 0% now. |
Movement
Since September 2026, Sybill held within the floor in all three categories: no change cleared 11 points.
Best category · Revenue intel · by segment
Mid-market▲+9Since September 2026: 0% → 9%, +9 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
9%from 0% in September 2026
Inside the floor by 2 pointsRank 30 → 5 of 68
EnterprisegoneNot named this edition; 0% in September 2026.
0%from 0% in September 2026
Inside the floor by 11 pointsNot ranked
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Revenue intel | 0% | 9% | ▲+9Since September 2026: 0% → 9%, +9 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 30 → 5 of 76 |
| Conversation intel | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 24 of 91, unchanged |
| Sales engagement | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 43 of 66, unchanged |
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 11 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 Sybill 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 Sybill 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 | 0 | 0 | 0 |
| Perplexity Sonar | 1 | 0 | 0 | 0 | 1 |
| Grok 4.1 Fast | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 1 | 0 | 0 | 0 | 1 |
| DeepSeek V4 Flash | 0 | 1 | 0 | 0 | 1 |
| Llama 4 Maverick | 1 | 1 | 0 | 0 | 2 |
| Qwen 3.7 Flash | 1 | 0 | 0 | 0 | 1 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| 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 | 0 | 1 | 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
Direct7 labelsNone
Paraphrase3 labelsNone
Comparative9 labels4not counted in share
Budget-constrained11 labels8
Scale-constrained3 labelsNone
Negative5 labelsNone
First choiceAlternativeMentionNegative38 labels in all, every segment counted; 8 of the 12 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.
“The best revenue intelligence and forecasting platform for a company with a limited budget is Sybill” Llama 4 Maverick · Revenue intel · budget prompt · first choice
“Sybill stands out as the top choice for budget-conscious buyers” Mistral Small · Revenue intel · budget prompt · first choice
“Sybill is generally considered the best all-around value” Qwen 3.7 Flash · Revenue intel · budget prompt · first choice
“the strongest all-around choice in the results is Sybill” Perplexity Sonar · Revenue intel · budget prompt · first choice
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 Sybill, over the 38 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Sybill was named but was not.ShowHide
Named alongside
The products named in the same answers as Sybill, over the 38 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Sybill 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 Sybill. 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 Sybill and are not a share of its labels.
Domains cited
sybill.aiYour site29
avoma.com22
pipeline.zoominfo.com18
optif.ai15
salesmotion.io15
cleverly.co13
zapier.com13
demandbase.com12
g2.com12
learn.g2.com12
132 of the 161 domain citations in answers naming Sybill came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
sybill.ai is not in Google's top ten for any of the category's searches. In the answers, Sybill takes 8% of first choices and Clari Copilot takes 28%.ShowHide
Search and answers
sybill.ai is not in Google's top ten for any of the category's searches. In the answers, Sybill takes 8% of first choices and Clari Copilot takes 28%.
ShowHideIn search
Google, US estimatesPosition for “revenue intelligence and forecasting platform”
–
not in the top ten
Position for “best revenue intelligence and forecasting platform”
–
not in the top ten
- Searches for its name, Google
- 2,900 a month (“sybill”)
- AI search demand for its name, est.
- 5,864 a month
- Organic visits to its site
- about 17,438 a month
- Searches its site ranks for
- 2,936 · 98 in the top three
- Sites linking to it
- 1,515
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
In answers
This editionShare of first choices
8%
rank 5 of 76 in revenue intel
Segment leader
28%
Clari Copilot
- First choices
- 8 across its categories
- Named in
- 38 answers
- Its own site cited
- in 38 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 sybill.ai
1,183
subdomains included
Content
915
counted in the table below
Documentation
129
Product · Integration
4 · 0
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 608 | undated | CRM, Revenue intel | ||
| Comparison | 146 | undated | CRM, Sales engagement | ||
| Template or tool | 130 | undated | CRM | ||
| Glossary or explainer | 16 | undated | Dialers, CPQ | ||
| Case study | 7 | undated | |||
| Conference or event | 4 | undated | |||
| Report or ebook | 2 | undated | |||
| Webinar or virtual event | 1 | undated | |||
| Podcast or video | 1 | undated |
How it is countedHide how it is counted
Every page sybill.ai 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. Of its titles that name a category, most name CRM (25); 4 name its categories the models place it in. 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.
Follow Sybill
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
Sybill is its own company.ShowHide
The company
Sybill is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- AI Sales Assistant for Revenue Teams
- For
- Revenue teams
- Price stated
- Free $0USD/Month/User; Pro $30USD/Month/User; Business $90USD/Month/User; Enterprise Custom; also shown: Pro $36USD/Month/User; Business $108USD/Month/User sybill.ai/pricing
- Starting price
- 0 sybill.ai/pricing
- Free plan or trial
- 14 days free trial sybill.ai
- Integrations claimed
- 3000 sybill.ai
- Certifications
- SOC 2GDPRISO 27001PCIISO/IEC 27001:2022PCI DSSCSA STAR Level 1EU-US DPF
- Customers named
- beehiivLushaSewer AlWorkatoSmartcatWarmlySoproActivate the Magic
- Not stated on the pages read
- Hosting
What Sybill's own pages state, read October 5, 2026: sybill.ai, sybill.ai, sybill.ai/pricing, trust.sybill.ai, sybill.ai/use-case/sales-leader. 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 Sybill'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 Sybill, 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 Sybill by email, built from the raw record of the edition. It shows:
- where Sybill 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 Sybill, 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.