We detected 31 customers using Sifflet. The most common industry is IT Services and IT Consulting (17%) and the most common company size is 1,001-5,000 employees (27%). Our methodology involves discovering URLs with known URL patterns through web crawling, certificate transparency logs, or modifications to subprocessor lists.
About Sifflet
Sifflet provides AI-augmented data observability that monitors data quality across the entire stack, detecting anomalies, assessing business impact, identifying root causes, and alerting teams to ensure reliable data from ingestion to consumption for both technical and non-technical users.
🔧 What other technologies do Sifflet customers also use?
Source: Analysis of tech stacks from 31 companies that use Sifflet
Commonly Paired Technologies
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Shows how much more likely Sifflet 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 something fascinating about companies using Sifflet. They're clearly enterprise organizations that have moved beyond basic tooling and are investing heavily in governance, security, and operational efficiency. The presence of tools like Netskope for cloud security and Luminance for AI-powered contract analysis tells me these are companies dealing with complex data environments where risk management isn't optional. They're likely in regulated industries or handling sensitive information at scale.
The pairing of Sifflet with Golinks is particularly revealing. When companies need internal link shortening, it means they have enough tools and resources that discoverability becomes a problem. Add Sifflet's data observability into that mix, and you see organizations wrestling with data sprawl across multiple systems. Similarly, Go1's learning platform appearing alongside Sifflet suggests these companies are actively upskilling employees to handle increasingly complex data operations. They recognize that technology alone won't solve their data quality challenges without proper training.
The full stack paints a picture of mature, likely growth-stage companies that are sales-led or facing enterprise compliance requirements. The investment in Telus Health for employee wellness and Powtoon Enterprise for video communications indicates they're building out comprehensive employee programs, something typical of companies past the scrappy startup phase. These organizations have the budget and foresight to invest in preventing problems rather than just fixing them, which is exactly what data observability provides.
👥 What types of companies is most likely to use Sifflet?
Source: Analysis of Linkedin bios of 31 companies that use Sifflet
I noticed that Sifflet's customers span an incredibly diverse range of operations, but they share a common thread: they're large-scale operations managing complex systems. These aren't simple businesses. They include global shipping and logistics networks like CMA CGM moving 23 million containers annually, retail giants like Carrefour with 9,900 stores, pharmaceutical manufacturers like Sanofi, energy companies like TotalEnergies, and media companies like Prisma Media reaching 40 million French users monthly. What unites them is operational complexity at scale.
These are overwhelmingly mature, established enterprises. The employee counts tell the story: twelve companies have over 1,000 employees, and eight have over 10,000. Many are publicly traded or backed by private equity. When startups appear in the list, like DataGalaxy and Atlan, they're in the data infrastructure space itself, suggesting they understand the data quality challenges from the inside. The funding rounds (Series C, post-IPO) and revenue figures (Carrefour generating billions) indicate companies operating at enterprise scale.
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