How Artie is Building a Real-Time Data Streaming Platform

Artie, redefining data replication for the modern era, led by Robin and Jacqueline, has raised a $12M Series A to continue building the fastest, most reliable way to move data in real-time led by Dalton Caldwell at Standard Capital, with continued participation from Y Combinator, Pathlight Ventures, and angel investors including Arash Ferdowsi (Dropbox founder & CTO), Benn Stancil (Mode founder & CTO), Chris Best (Substack founder & CEO), Charles Hearn (Alloy founder & CTO), and Lenny Rachitsky (Lenny’s Podcast).

AI has pushed data into live systems that drive decisions, workflows, and customer interactions, making data freshness critical. Real-time data enables accurate support responses, automated financial and operational workflows, and compliant onboarding decisions. Teams using real-time data move faster and deploy AI successfully, while others remain stuck in pilots, as the gap between streaming and batch data increasingly impacts product velocity, reliability, and cost.

Moving high volumes of data in real time is complex, as building reliable streaming pipelines requires handling transactional integrity, failure recovery, monitoring, and production safety. Despite extensive engineering effort, gaps often remain in observability and edge-case handling. Such pipelines create ongoing engineering overhead, high maintenance costs, and operational risk concentrated among a small group of domain experts.

Artie is a fully managed real-time data streaming platform that delivers fresh, accurate data across systems to power AI products. It provides production-grade streaming pipelines comparable to those built by leading technology companies, without the need for dedicated engineering teams to build and maintain the infrastructure.

Fast-growing companies use Artie to process hundreds of billions of data rows annually, powering AI agents, recommendation systems, and embedded analytics. The next generation of data infrastructure is streaming-first, driven by the demands of AI-powered products.

For teams building real-time data products, operational complexity around schema evolution, transactional integrity, failure recovery, and out-of-order data handling is removed. This reduces risk, eliminates reliance on specialized internal knowledge, and accelerates the path from experimentation to production. Engineering effort shifts from maintaining infrastructure to shipping AI-enabled features on trusted data.

With the new funding, investment will focus on expanding product capabilities and scaling go-to-market efforts. Platform support will broaden to include event APIs, search systems, vector databases, and additional data systems, positioning streaming as a shared foundation across the stack. Go-to-market efforts will prioritize a self-serve onboarding experience alongside enterprise-grade BYOC deployments, 24/7 support, and security, with SOC 2 Type II and HIPAA compliance maintained.