We detected 166 customers using Swan and 47 companies that churned or ended their trial. The most common industry is Software Development (42%) and the most common company size is 11-50 employees (54%). Our methodology involves detecting JavaScript snippets or configurations on customer websites.
Note: We only track when a company installs the Swan tracking script on their website (majority of customers)
About Swan
Swan identifies and de-anonymizes website visitors using AI agents, then automatically qualifies them against ideal customer profiles and executes personalized outreach on LinkedIn and email to convert anonymous traffic into sales meetings.
🔧 What other technologies do Swan customers also use?
Source: Analysis of tech stacks from 166 companies that use Swan
Commonly Paired Technologies
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Shows how much more likely Swan customers are to use each tool compared to the general population. For example, 287x means customers are 287 times more likely to use that tool.
I noticed that Swan users are running aggressive, modern outbound sales operations with a heavy emphasis on website visitor identification and account-based prospecting. The massive correlation with tools like RB2B, Warmly, and Vector.co tells me these companies are obsessed with capturing buying intent signals the moment someone lands on their website. They're not waiting for forms to be filled out. They want to know who's browsing and reach out immediately.
The pairing of RB2B with Warmly is particularly revealing. RB2B identifies anonymous website visitors at the company level, while Warmly takes that data and turns it into immediate sales actions. Add Midbound to the mix, which focuses on catching prospects when they're actually in-market, and you see a clear pattern. These companies are building real-time prospecting machines that react to buying signals within minutes, not days. The presence of Knock AI, which automates personalized outreach, suggests they're trying to operate this motion at scale without drowning their sales teams in manual work.
The full stack screams sales-led growth at the early to mid-stage startup level. These aren't enterprise companies with long-established processes. They're startups trying to punch above their weight class by being faster and smarter about identifying opportunities. The Salesforce App Exchange correlation confirms they're investing in CRM infrastructure, but the focus on visitor identification and intent data shows they're hunting for efficiency gains in the top of funnel, not just optimizing existing pipeline.
👥 What types of companies is most likely to use Swan?
Source: Analysis of Linkedin bios of 166 companies that use Swan
Company Characteristics
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Shows how much more likely Swan customers are to have each trait compared to all companies. For example, 2.0x means customers are twice as likely to have that characteristic.
Trait
Likelihood
Funding Stage: Seed
54.4x
Industry: Software Development
18.5x
Industry: Technology, Information and Internet
15.8x
Company Size: 51-200
3.4x
Country: US
3.0x
Company Size: 11-50
2.3x
I analyzed these companies and found that Swan's typical customers are B2B software and technology companies building platforms that other businesses depend on. They're not selling to consumers. They're creating infrastructure for financial services, building developer tools, automating business operations, or providing specialized SaaS solutions. Many are in fintech, but you also see heavy representation in cybersecurity, cloud infrastructure, procurement automation, and workforce management.
These are predominantly growth-stage companies. The funding data tells part of the story: most have raised Seed through Series B rounds, typically between 5 million and 50 million dollars. Employee counts cluster between 11 and 200 people. These aren't scrappy pre-product startups, nor are they mature public companies. They're in that critical scaling phase where they have product-market fit and paying customers, but they're still building out infrastructure, adding features, and figuring out operations at scale.
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