We detected 117 customers using Expertise.ai, 67 companies that churned or ended their trial, and 11 customers with estimated renewals in the next 3 months. The most common industry is Software Development (16%) and the most common company size is 11-50 employees (33%). Our methodology involves detecting JavaScript snippets or configurations on customer websites.
About Expertise.ai
Expertise.ai provides an AI-powered demand conversion platform that engages website visitors proactively, qualifies leads in real time, identifies anonymous visitors, and integrates with CRM systems to convert traffic into sales pipeline automatically.
Appliances, Electrical, and Electronics Manufacturing4 (4%)
Hospitals and Health Care3 (3%)
📏 Company Size Distribution
11-50 employees37 (33%)
51-200 employees33 (29%)
2-10 employees19 (17%)
201-500 employees15 (13%)
501-1,000 employees5 (4%)
🔧 What other technologies do Expertise.ai customers also use?
Source: Analysis of tech stacks from 117 companies that use Expertise.ai
Commonly Paired Technologies
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Shows how much more likely Expertise.ai 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 companies using Expertise.ai are deeply invested in understanding user behavior and optimizing their digital experiences through advanced analytics. The overwhelming presence of PostHog and its feature flags, combined with Microsoft Clarity, tells me these are data-driven organizations that want granular visibility into how people interact with their products. They're not just tracking basic metrics but running experiments and making decisions based on real user behavior patterns.
The pairing of PostHog with its feature flags is particularly revealing. These companies aren't just measuring what users do but actively testing different experiences and rolling out features strategically. When I see this combined with Apollo.io's website visitor tracker, it suggests a sophisticated approach to converting anonymous traffic into known prospects. They're connecting product usage data with sales intelligence, which means they likely have a product-led growth motion where user behavior triggers sales outreach. The HubSpot tools reinforce this, giving them the infrastructure to nurture leads through both automated marketing and personalized conversations.
The full stack reveals companies in growth mode that are trying to optimize every part of their funnel. They're product-led in the sense that they care deeply about the user experience and feature adoption, but they're also using that product data to feed their sales and marketing efforts. This isn't a pure product-led or sales-led motion but rather a hybrid approach where product insights inform commercial strategy. These are likely Series A to Series B companies that have found some product-market fit and are now focused on efficient scaling.
👥 What types of companies is most likely to use Expertise.ai?
Source: Analysis of Linkedin bios of 117 companies that use Expertise.ai
Company Characteristics
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Shows how much more likely Expertise.ai 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
Company Size: 51-200
6.8x
Country: US
3.2x
Company Size: 11-50
2.7x
I noticed that Expertise.ai's customers span an incredibly diverse range of industries, but they share a common thread: they're service providers and solution implementers. These aren't manufacturing giants or pure product companies. Instead, they're IT consulting firms implementing SAP and cloud solutions, marketing agencies creating digital campaigns, distribution companies managing supply chains, professional services firms offering specialized expertise, and technology platforms enabling specific business functions. Many describe themselves as partners or consultants who help other businesses transform, optimize, or modernize their operations.
The company size data shows a clear pattern: most fall into the 11-50 or 51-200 employee range, with only a handful exceeding 500 employees. Very few disclosed funding, and when they did, it was typically seed or Series A stage with modest amounts. However, many appear to be established businesses rather than fresh startups. They mention decades of experience, thousands of customers, or long-standing market presence. These are growth-stage companies that have proven their model but are still scaling.
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