Fleak is a technology company that builds a serverless AI data workflow platform designed to simplify the creation, integration, and scaling of complex data workflows for data teams. Its platform serves data scientists, analysts, and engineers by automating infrastructure management, enabling them to focus on innovation rather than operational overhead. Fleak’s solution addresses challenges such as data drift, schema changes, and vendor lock-in by providing AI-native configuration generation and flexible deployment options across various environments, including on-premises and cloud. The platform supports seamless orchestration of multiple AI models, reducing latency and boosting efficiency in real-time data processing workflows, which is critical for growing businesses scaling their AI applications[1][2][3].
Fleak was founded by Yichen Jin and Bo Lei, who met through a shared passion for simplifying complex data workflows. Yichen brought expertise in machine learning and venture capital, having led data science teams focused on fraud detection, where she experienced firsthand the infrastructure challenges data scientists face. Bo Lei, a seasoned software engineer with experience at Nvidia, Splunk, and Netflix, contributed deep knowledge of real-time data pipelines, notably helping develop Netflix’s DataMesh platform. Their combined frustrations with infrastructure complexity and desire to give data teams more time for impactful work led to the creation of Fleak. Early traction included solving integration bottlenecks for cybersecurity platforms, dramatically reducing onboarding times and enabling significant revenue growth[1][3].
Fleak rides the growing trend of AI-driven data orchestration and automation in an era where AI applications and data workflows are rapidly scaling. The timing is critical as organizations face increasing complexity in managing diverse data sources, evolving schemas, and integrating multiple AI models efficiently. Market forces such as the rise of serverless computing, demand for real-time analytics, and the need to avoid vendor lock-in favor Fleak’s flexible, engine-agnostic approach. By simplifying infrastructure management and enabling seamless AI workflow orchestration, Fleak influences the broader ecosystem by accelerating AI adoption and innovation across industries, particularly in sectors reliant on real-time data insights and scalable AI solutions[1][2][3].
Looking ahead, Fleak is well-positioned to capitalize on the expanding demand for automated, scalable AI data workflows as enterprises increasingly embed AI into their core operations. Future trends shaping Fleak’s journey include advances in AI model integration, edge computing deployments, and enhanced data governance requirements. The company’s ability to maintain flexibility, reduce operational friction, and support multi-engine environments will likely drive broader adoption and deeper ecosystem influence. As AI workflows become more complex and critical, Fleak’s platform could evolve into a foundational infrastructure layer for AI-driven enterprises, empowering data teams to innovate faster and more reliably.
This trajectory ties back to Fleak’s founding mission: to give data teams back their time by transforming complex data processes into simple, scalable, and efficient workflows[1][2][3].
Fleak has raised $5.0M in total across 1 funding round.
Fleak's investors include Alpha Intelligence Capital, Alumni Ventures, GFT Ventures, Mana Ventures, Menlo Ventures, Scale Asia Ventures.
Fleak has raised $5.0M across 1 funding round. Most recently, it raised $5.0M Seed in September 2025.
| Date | Round | Lead Investors | Other Investors |
|---|---|---|---|
| Sep 1, 2025 | $5.0M Seed | Alpha Intelligence Capital, Alumni Ventures, GFT Ventures, Mana Ventures, Menlo Ventures, Scale Asia Ventures |