We detected 60 customers using Bland AI. The most common industry is Hospitals and Health Care (20%) and the most common company size is 51-200 employees (36%). Our methodology involves discovering URLs with known URL patterns through web crawling, certificate transparency logs, or modifications to subprocessor lists.
About Bland AI
Bland AI automates inbound and outbound phone calls using conversational AI that sounds human, serving enterprises for sales, customer support, and operations. Customers use Bland to build AI phone agents that handle call volume for customer support, sales, or appointment setting efficiently and affordably.
๐ง What other technologies do Bland AI customers also use?
Source: Analysis of tech stacks from 60 companies that use Bland AI
Commonly Paired Technologies
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Shows how much more likely Bland 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 companies using Bland AI have a distinctive pattern in their tech stacks that points to mature, operationally sophisticated B2B organizations. The combination of compliance tools like Navex One, learning management systems like Docebo, and performance management platforms like Lattice tells me these are established companies with formal HR infrastructure and serious regulatory requirements. They're not scrappy startups experimenting with AI for the first time.
The pairing of Bland AI with Rocketlane is particularly revealing. Rocketlane handles customer onboarding and implementation, which suggests these companies are using Bland AI to automate communications during complex sales cycles or post-sale customer journeys. When I see Looker Cloud alongside this, it indicates they're measuring the impact of their AI conversations through data analytics, treating voice AI as a performance metric rather than just a cost-saving tool. The presence of Docebo suggests they might even be training teams on how to work alongside AI systems or using AI to supplement their customer education efforts.
The full stack reveals these are sales-led or customer success-led organizations in growth mode but past their early stage. They have the resources to invest in enterprise-grade security (Perimeter81), the complexity that requires sophisticated onboarding workflows (Rocketlane), and the team size that demands formal performance reviews (Lattice). These aren't product-led growth companies with viral adoption. They're building structured, repeatable processes for customer acquisition and retention.
๐ฅ What types of companies is most likely to use Bland AI?
Source: Analysis of Linkedin bios of 60 companies that use Bland AI
Company Characteristics
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Shows how much more likely Bland 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
Country: US
4.7x
I noticed that Bland AI's customers span an incredibly wide range, but they share a common thread: they're all in high-touch, high-volume communication businesses. These aren't pure software companies building tools for developers. They're healthcare providers coordinating patient care, insurance companies explaining benefits, home services companies scheduling appointments, restaurants taking reservations, law firms consulting with veterans, and financial services helping people navigate complex decisions. What unites them is the need to handle thousands of inbound inquiries, schedule appointments, qualify leads, or guide customers through complicated processes.
These companies range dramatically in maturity. I see early-stage startups with 6-28 employees and seed funding sitting alongside Fortune 500 enterprises with 10,000-plus employees. However, the majority fall into a sweet spot: growth-stage companies with 50-500 employees, often Series A through Series C funded, or established businesses with 1,000-5,000 employees that are modernizing their operations. Even the largest enterprises here seem to be in transformation mode, not resting on legacy systems.
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