We detected 466 companies using Fireflies AI. The most common industry is Software Development (14%) and the most common company size is 51-200 employees (38%). We find new customers by monitoring new entries and modifications to company DNS records.
Note: We only track companies who subscribe to the Enterprise version
Source: Analysis of Linkedin bios of 466 companies that use Fireflies AI
Company Characteristics
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Shows how much more likely Fireflies 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
Funding Stage: Private equity
62.2x
Funding Stage: Series B
43.4x
Funding Stage: Series unknown
27.3x
Industry: Investment Management
23.4x
Industry: Biotechnology Research
17.5x
Industry: Venture Capital and Private Equity Principals
15.6x
I noticed that Fireflies AI attracts an incredibly diverse range of companies, from museums and sports marketing agencies to banks, law firms, medical equipment manufacturers, and technology platforms. What unites them is less about what they build and more about how they work. These are organizations where meetings, client interactions, and cross-functional collaboration drive their core operations. Whether it's EC-Council delivering cybersecurity training, IMEG providing engineering consulting, or Altruist serving financial advisors, these companies rely heavily on conversations with clients, internal coordination, and knowledge transfer.
These are predominantly established, mid-market to enterprise organizations. The employee counts cluster heavily in the 51-200 and 201-500 ranges, with many companies describing decades of history and proven track records. Several mention being industry leaders, having extensive client bases, or operating multiple offices. Even the smaller employee-count companies often describe substantial market presence or specialized expertise. The funding stages, where listed, skew toward later stages like private equity, series rounds beyond A, or post-IPO. These are not scrappy startups testing product-market fit.
🔧 What other technologies do Fireflies AI customers also use?
Source: Analysis of tech stacks from 466 companies that use Fireflies AI
Commonly Paired Technologies
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Shows how much more likely Fireflies 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 Fireflies AI users are overwhelmingly AI-forward companies that have embraced modern collaboration and automation tools. The extreme correlation with Claude for Work and ChatGPT for Teams tells me these are organizations that don't just experiment with AI, they've institutionalized it across their operations. Combined with tools like Cursor, Notion Enterprise, and Zapier Enterprise, this points to companies running lean, highly automated operations where technology replaces traditional headcount.
The pairing of Fireflies with Cursor is particularly revealing. Cursor users are typically engineering teams moving fast and building products quickly. When you add Fireflies to capture meetings alongside ChatGPT and Claude for distributed work, you see teams that prioritize asynchronous communication and documentation. The Zapier Enterprise correlation reinforces this, these companies are connecting their tools together to create workflows that run without manual intervention. Notion Enterprise appearing so frequently suggests they're also centralizing knowledge and making meeting insights immediately actionable through connected databases.
My analysis shows these are likely product-led or hybrid growth companies, probably Series A through Series C stage. They're past the scrappy startup phase but haven't calcified into enterprise bureaucracy. The presence of Asana Enterprise alongside automation tools suggests coordinated teams working across functions, not massive hierarchical organizations. They're sales-assisted rather than purely sales-led, using tools to scale efficiency rather than building large go-to-market teams.
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