Typedef emerges from stealth for $5.5 million to turn AI prototypes into production grade pipelines

AI startup Typedef is focused on turning AI prototypes into production workloads, coming out of stealth with a $5.5 million seed funding. The round was led by Pear VC, sponsored by groups such as Verissimo Ventures, Monochrome Ventures, Tokyo Black and Angel Investors.

The company is run by co-founders Yoni Michael and Costas Paldalis. Both have deep roots in data infrastructure. Michael previously sold Coolan, a data center analytics startup, to Salesforce in 2016. The two call themselves “data nerds” and are aiming for the $200 billion AI infrastructure market.

“We are also very proud to announce the $5.5 million seed round led by Pear VC, with support from Verissimo Ventures, Monochrome, Tokyo Black and the exceptional group Angels. We are extremely fortunate to share our AI infrastructure, and we are able to share our AI infrastructure, which we imagine from our blog.

Their pitch is simple: too many AI projects should not pass the prototype stage. Typedef was built from the ground up to handle the troublesome reality of production AI. Runs large models, manages unstructured data, and deals with workloads that don’t behave unexpectedly.

“Inference is a new transformation,” the company says. It mentions the transition from a training model to actually running at scale. The team has experienced key infrastructure efforts in businesses such as Salesforce, Tecton and Starburst Data, and poured it into Typedef. This is a tool to help teams move beyond AI setups of duct tapes that break under real use.

“Typedef is now enabling teams to build reliable, scalable, and cost-effective leading language model (LLM) workloads that are reliable, scalable, and cost-effective, large language model (LLM) workloads, with no complexity or tension in managing infrastructure.”

Most AI projects never leave the prototype phase

That’s not just a premonition. AI pilot projects are stalling everywhere. According to an Informatica survey, 93% of US data leaders are increasing their Genai budget next year, while 67% say they were unable to even transfer half of their pilots to production. Some estimates show that 87% of AI projects fail to scale.

“There’s no clear path to production,” Michael said. “Legacy systems weren’t built for LLMS or unstructured data. So people are piecing together outdated tools, custom scripts and the best hopes. But the results are usually brittle and unreliable.”

Typedef is trying to fix it. Teams can build, test, and deploy AI pipelines. This allows for much less workflows and much less friction to process and analyze data using LLM. This was constructed to be deterministic on a nondeterministic model. This means that teams can trust what they ship.

What Typedef does

Typedef is a core, helping developers run scalable LLM pipelines for things like semantic analysis without worrying about all the messy pieces like token limits, context windows, data chunks and more. Its interface is simple, with APIs and relational models already familiar to engineers.

It’s completely serverless. Infrastructure setup, no provisioning, no configuration files. Download the open source client library, hook it into the data source, and start building the pipeline with a few lines of code.

“The team wants the same reliability from the AI ​​pipelines they’ve had in traditional data pipelines,” Pardalis said. “They want to use AI to run analytics workloads, extract insights from their own data, and do it at every stage without fighting the tools.”

Early towing from real customers

Matic, an insurance technology company that works with over 70 airlines, has already used Typedef for production. They have built an AI workflow to handle policy documents and customer support transcripts. Results: Fast processing, fewer human errors, and lower compliance risks.

“With Typedef, you can build and deploy semantic extraction pipelines across thousands of policies and transcripts rather than months,” said Lee Maliniak, Chief Product Officer at Matic.

What’s next for Typedef?

Typedef aims to be an infrastructure layer for businesses attempting to operate AI, especially when it includes large models, unstructured data, or complex workflows. Startups believe their stripped production response approach is what companies need to ultimately get out of AI pilot purgatory.