Mid-market▲+2Since September 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
4%from 2% in September 2026
Inside the floor by 9 pointsRank 12 → 8 of 109
3 categories · RankedDecagon
22Judge labels
6First choices
7Negative 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
5% in Support chatbots for mid-market buyers
Rank 4 of 114 in the mid-market standing
3 of 14 models made it the first choice on the direct prompt; 25% of its 8 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small businessnot named
0%
No model named it for this buyer.
Mid-market4 of 114
5%8 labels
under the label threshold
Enterprise31 of 113
0%2 labels
under the label threshold
In support chatbots · 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 Decagon 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 |
|---|---|---|---|---|---|---|---|
| Chatbots and conversational AI for support | Customer | 5% | 4 of 114 | 25% | 8 | under 10 labels · led by Intercom Fin AI Agent at 41% | ▲+2Since September 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| AI voice agents | Customer | 0% | 83 of 103 | 100% | 1 | under 10 labels · led by Retell AI at 27% | |
| Conversational marketing platforms | Marketing | 0% | 62 of 87 | 0% | 1 | under 10 labels · led by Tidio at 13% |
Movement
Since September 2026, Decagon held within the floor in all three categories: no change cleared 11 points.
Best category · Support chatbots · by segment
Enterprise▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 2% in September 2026
Inside the floor by 9 pointsRank 13 → 37 of 105
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Support chatbots | 2% | 4% | ▲+2Since September 2026: 2% → 4%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 12 → 4 of 114 |
| AI voice agents | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 83 of 103, unchanged |
| Conversational mktg | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 62 of 87, 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 Decagon 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 Decagon 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 | 2 | 0 | 2 |
| Perplexity Sonar | 0 | 0 | 0 | 1 | 1 |
| Grok 4.20 | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 1 | 0 | 0 | 0 | 1 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 2 | 2 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 0 | 1 |
| Kimi K2 | 1 | 0 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 1 | 0 | 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 labels4
Paraphrase4 labels2
Comparative1 labelNone
Budget-constrained0 labelsNone
Scale-constrained3 labelsNone
Negative6 labelsNone
First choiceAlternativeMentionNegative22 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.
“Choose Decagon if you want best-in-class autonomous support AI with lower pricing than enterprise leaders” Muse Glimmer 30B · Support chatbots · direct prompt · first choice
“Zendesk Copilot and Decagon are the strongest recommendations for most mid-market B2B use cases.” Mistral Small · Support chatbots · direct prompt · first choice
“Start with Decagon if you want strong AI capabilities at competitive pricing” Kimi K2 · Support chatbots · direct prompt · first choice
“Best for Complex "AI Agents": Decagon” Qwen 3.7 Flash · Support chatbots · 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.
“Probably overkill for most mid-market B2B companies unless you have exceptional ticket volume.” DeepSeek V4 Flash · Support chatbots · direct prompt · soft negative
“cautious if you want a self-serve platform, transparent pricing, or a simple setup” Perplexity Sonar · AI voice agents · negative prompt · soft negative
“High-end enterprise AI agents | Premium pricing ($150K+ annually)” DeepSeek V4 Flash · Support chatbots · scale prompt · soft negative
Named alongside
The products named in the same answers as Decagon, over the 22 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Decagon was named but was not.ShowHide
Named alongside
The products named in the same answers as Decagon, over the 22 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Decagon 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 Decagon. 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 22 of the 22 answers that named Decagon and are not a share of its labels.
Domains cited
retellai.com8
botpress.com7
cloudtalk.io7
zendikt.com7
wonderchat.io6
fin.ai5
g2.com5
getvoip.com5
synthflow.ai5
assembled.com4
Fifty-nine of the fifty-nine domain citations in answers naming Decagon came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
decagon.ai is not in Google's top ten for any of the category's searches. In the answers, Decagon takes 5% of first choices and Intercom Fin AI Agent takes 41%.ShowHide
Search and answers
decagon.ai is not in Google's top ten for any of the category's searches. In the answers, Decagon takes 5% of first choices and Intercom Fin AI Agent takes 41%.
ShowHideIn search
Google, US estimatesPosition for “customer support chatbot platform”
–
not in the top ten
Position for “best customer support chatbot platform”
–
not in the top ten
- Searches for its name, Google
- 60,500 a month (“decagon”)
- AI search demand for its name, est.
- 1,155 a month
- Organic visits to its site
- about 62,756 a month
- Searches its site ranks for
- 2,906 · 314 in the top three
- Sites linking to it
- 1,987
- Paid Google search
- 1 searches its ads show for, about 1,815 clicks and $47,716 a month (estimate)
In answers
This editionShare of first choices
5%
rank 4 of 114 in support chatbots
Segment leader
41%
Intercom Fin AI Agent
- First choices
- 6 across its categories
- Named in
- 22 answers
- Its own site cited
- in 22 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 decagon.ai
563
subdomains included
Content
481
counted in the table below
Documentation
0
Product · Integration
12 · 0
| Kind | Pages | Last 90 days | 2025-11 to 2026-10 | Latest | Categories named |
|---|---|---|---|---|---|
| Glossary or explainer | 261 | undated | |||
| Blog | 161 | 37 | 2026-10-02 | ||
| Case study | 23 | 23 | 2026-09-25 | ||
| Podcast or video | 19 | 2 | 2026-07-27 | ||
| Comparison | 11 | 2 | 2026-09-02 | ||
| Report or ebook | 6 | 1 | 2026-09-09 |
Most recent
- 2026-10-02Decagon and open ai blog
- 2026-10-01Agent modules blog
- 2026-10-01Contact center analytics blog
- 2026-10-01Decagon chord voice model blog
- 2026-10-01Dialogues 2026 blog
- 2026-10-01Duet apprentice blog
How it is countedHide how it is counted
Every page decagon.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. Read October 5, 2026.
Follow Decagon
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
Decagon is its own company.ShowHide
The company
Decagon is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- The AI concierge for every customer
- For
- the enterprise
- Certifications
- SOC 2 Type IIGDPRCCPAHIPAAEU AI ACTISO 27001:2022PCI DSS 4.0.1
- Hosting
- USAEU
- Customers named
- ChimeRipplingClassPassNotionEventbriteOuraBiltAffirm
- Not stated on the pages read
- Price, starting price, free plan or trial, integrations
What Decagon's own pages state, read October 5, 2026: decagon.ai, trust.decagon.ai, decagon.ai/product/aop, decagon.ai/case-studies, decagon.ai/product/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 Decagon'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 Decagon, 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 Decagon by email, built from the raw record of the edition. It shows:
- where Decagon 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 Decagon, 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.