We detected 64 companies using Level AI. The most common industry is Software Development (15%) and the most common company size is 1,001-5,000 employees (28%). We find new customers by discovering URLs with known URL patterns through web crawling or modifications to subprocessor lists.
Source: Analysis of Linkedin bios of 64 companies that use Level AI
I noticed Level AI's customers are predominantly companies that handle massive volumes of customer interactions. These aren't just any B2C businesses. They're in industries where customer service is make-or-break: financial services like Wealthsimple, insurance and protection services like Assurant and Asurion, real estate platforms like Zillow and Airbnb, healthcare providers, energy utilities, and manufacturing marketplaces. What unites them is that they're managing millions of customer touchpoints where quality and efficiency directly impact revenue.
My analysis shows these are primarily mature, established enterprises. Seven of the ten are either public companies, post-IPO, or have 1,000-plus employees. Assurant and Asurion each have over 16,000 employees. Even the smaller companies like Purity Products emphasize their "25+ years" in business. The funding stages skew late, with Series E, post-IPO debt, and public companies dominating. These aren't scrappy startups testing product-market fit. They're scaled operations with established customer bases.
🔧 What other technologies do Level AI customers also use?
Source: Analysis of tech stacks from 64 companies that use Level AI
Commonly Paired Technologies
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Shows how much more likely Level 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 Level AI users are overwhelmingly customer service and contact center operations companies. The pattern is unmistakable: these are businesses that handle massive volumes of customer interactions and need sophisticated tools to analyze conversations, manage agent performance, and optimize their support operations at scale.
The pairing with Five9, a leading cloud contact center platform, immediately tells me these companies run serious customer service operations with distributed teams handling calls, chats, and digital channels. When I see Decagon AI appearing alongside Level AI, it suggests these teams are exploring AI-powered customer service automation while still maintaining human agents for complex issues. The Visier connection is particularly revealing because it's a people analytics platform, which means these companies are deeply invested in workforce planning and agent performance management. They're not just answering tickets randomly. They're treating customer service as a strategic function worth analyzing and optimizing.
My analysis shows these are operations-focused, scale-stage companies. The presence of Alert Media (emergency communications) and Watershed (carbon accounting) suggests they're large enough to care about enterprise risk management and ESG compliance. This isn't the tech stack of early-stage startups experimenting with support tools. These are mature organizations with hundreds or thousands of customer service agents, complex compliance requirements, and executive teams that view customer experience as a competitive differentiator. They're likely post-Series B at minimum, possibly public companies given the enterprise-grade compliance tools.
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