Typedef Launches with $5.5 Million in Seed Funding to Help Deliver on the Promise of AI with Production-Ready Workloads Built for Scale

 Typedef Inc., turning AI prototypes into scalable, production-ready workloads that generate immediate business value, today has come out of stealth mode with $5.5 million in seed funding led by early stage investors Pear VC with participation from Verissimo Ventures, Monochrome Ventures, Tokyo Black, and several angel investors.

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The Artificial Intelligence (AI) infrastructure market is projected to reach $200 billion by 2028. With a new purpose-built AI data infrastructure for modern workloads, Typedef is helping AI and data teams overcome the well-documented epidemic affecting the bulk of enterprise AI projects – failure to scale.

“It is extremely difficult to put AI workloads into production in a predictable, deterministic and operational way, causing most AI projects to linger in the prototype phase – failing to achieve business value or demonstrate ROI,” said Yoni Michael, Co-founder of Typedef. “The fact is, legacy data platforms weren’t built to handle LLMs, inference, or unstructured data. As a result, the workaround has been a patchwork of systems, aging technologies and tooling, or DIY frameworks and data-processing pipelines that are brittle, unreliable, and don’t scale. Typedef is righting these wrongs with a solution built from the ground up with features to build, deploy, and scale production-ready AI workflows – deterministic workloads on top of non-deterministic LLMs.”

Typedef makes it easy to run scalable LLM-powered pipelines for semantic analysis with minimal operational overhead. The developer-friendly solution manages all the complex properties of mixed AI workloads like token limits, context windows, and chunking through a clean, composable interface with the APIs and relational models engineers recognize. Typedef allows for rapid, iterative prompt and pipeline experimentation to quickly determine production-ready workloads that will demonstrate value – then realize that potential at scale.

“Data complexities and flawed data inputs are common obstacles on the journey to AI-readiness,” said Kostas Pardalis, Co-founder of Typedef. “AI and data teams want the same rigor and reliability they expect from traditional data pipelines. They want to supercharge their online analytic processing (OLAP) workloads with AI, extract new value from proprietary data, and run complicated agentic workloads with predictability and scalability. Typedef is making this possible, allowing teams to finally deliver on their AI promises to stakeholders.”

Typedef is completely serverless bypassing any infrastructure provisioning or configuration. Users simply download the open-source client library, connect their data sources and start building their AI or agentic pipelines with just a few lines of code. No complex setup, no infrastructure to provision, no brittle custom integrations to troubleshoot.

“Typedef lets us build and deploy semantic extraction pipelines across thousands of policies and transcripts in days not months,” said Lee Maliniak, Chief Product Officer at Matic, a leading insurance-tech platform that partners with top-rated carriers. “We’ve dramatically reduced the time it takes to eliminate errors caused by human analysis, significantly cut costs, and lowered our Errors and Omissions (E&O) risk.”

“Typedef is ushering in the new era of AI infrastructure where model training has given way to inference and where teams can build reliable, scalable, and cost-effective Large Language Model (LLM) workloads without the complexity or strain of managing infrastructure,” said Arash Afrakhteh, Partner at Pear VC. “I’ve backed this team because they’ve lived the problem, know what’s needed, and have the added experience of running data infrastructure startups to successful exits.”

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