Companies that use Gentrace

Analyzed and validated by Henley Wing Chiu

Gentrace We detected 4 customers using Gentrace. The most common industry is Software Development (100%) and the most common company size is 1,001-5,000 employees (50%). Our methodology involves monitoring new entries and modifications to company DNS records.

Note: Our data specifically only tracks Gentrace Enterprise users.

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Company Domain Employees Industry Region YoY Headcount Growth Usage Start Date
Faire 1,001–5,000 Software Development US +7.1%
Webflow 501–1,000 Software Development US +11.7%
Asana 1,001–5,000 Software Development US +10.2%
Gentrace 2–10 Software Development N/A 0%
Showing 1-20 of 4

Market Insights

🏢 Top Industries

Software Development 4 (100%)

📏 Company Size Distribution

1,001-5,000 employees 2 (50%)
2-10 employees 1 (25%)
501-1,000 employees 1 (25%)

👥 What types of companies is most likely to use Gentrace?

Source: Analysis of Linkedin bios of 4 companies that use Gentrace

I noticed that Gentrace's customers are primarily established software companies building sophisticated digital products and platforms. These aren't just any tech companies. They're creating tools that other businesses depend on: wholesale marketplaces connecting brands and retailers, website building platforms, and work management systems. They're in the business of enabling productivity and commerce for their own customers.

These are scaling growth companies and mature enterprises, not early startups. The employee counts tell part of the story: ranging from 1,471 to 4,156 employees at customer companies. They've raised significant capital, with Webflow's $120M Series C and later stage funding rounds. Asana mentions serving "more than 170,000 organizations" including major enterprises. These companies have moved well past product-market fit and are operating at serious scale.

A salesperson should understand that Gentrace's typical customer has already integrated AI into their core product and now faces the operational challenge of maintaining reliability at scale. These aren't companies experimenting with AI. They're companies whose businesses depend on AI working consistently for thousands or millions of end users. They need testing and evaluation tools because their AI systems are mission-critical, not nice-to-have features. The stakes are high, and quality control matters deeply to their business outcomes.

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