Typedef is a company that turns AI prototypes into scalable and production-ready workloads, generating immediate business value. The company has emerged from 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.
What Typedef does: 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, which is failure to scale.
Typedef makes it easier 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, such as token limits, context windows, and chunking, through a clean, composable interface that engineers recognize, utilizing APIs and relational models. Typedef enables for rapid and iterative prompt and pipeline experimentation to quickly determine production-ready workloads that will demonstrate value and then realize that potential at scale.
Typedef is completely serverless, bypassing infrastructure provisioning and configuration. And 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.
Company founders: Typedef Co-founders Kostas Pardalis and Yoni Michael are data infrastructure engineers turned serial entrepreneurs with Michael selling his prior company, Coolan to Salesforce in 2016.
KEY QUOTES:
“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. 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.”
Yoni Michael, Co-founder of Typedef
“Data complexities and flawed data inputs are common obstacles on the journey to AI-readiness. 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.”
Kostas Pardalis, Co-founder of Typedef
“Typedef lets us build and deploy semantic extraction pipelines across thousands of policies and transcripts in days not months. 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.”
Lee Maliniak, Chief Product Officer at Matic, a leading insurance-tech platform that partners with top-rated carriers
“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. 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.”
Arash Afrakhteh, Partner at Pear VC
