Bridging the AI Gap: How Yoni Michael's Typedef is Revolutionizing Data Processing
Yoni Michael, co-founder of Typedef, is a seasoned tech innovator with a rich background in infrastructure engineering, large-scale systems, and AI/ML platforms. With experience spanning roles at industry giants like Tecton and Salesforce, as well as his own startup Coolan, Michael has consistently been at the forefront of turning complex infrastructure into user-friendly, scalable products.
In an exclusive interview with AI World Today, Michael shares his journey from leading infrastructure teams to co-founding Typedef, a cutting-edge serverless data platform designed to revolutionize how teams handle AI workloads. His insights reveal how Typedef is addressing the critical gap between AI prototypes and production-ready systems, offering a solution that combines the simplicity of traditional ETL with the power of large language models (LLMs).
Michael's vision for Typedef stems from his diverse experience in data center infrastructure, machine learning, and AI. He recognized a recurring pattern: powerful new primitives emerge, but widespread adoption is hindered until the right abstractions and infrastructure are in place. With Typedef, Michael and his team are creating those crucial abstractions for LLM inference, potentially catalyzing the next wave of AI adoption in enterprise settings.
1. Could you start by introducing yourself and giving us a brief overview of your background in the tech industry?
I’m Yoni, co-founder of Typedef. Before this I led infrastructure engineering at Tecton, worked on large-scale systems at Salesforce (after my first startup, Coolan, was acquired), and have spent my career at the intersection of data platforms and ML/AI. My focus has been turning complex, high-scale infrastructure into simple, reliable products that real teams can adopt quickly.
2. What inspired you to co-found Typedef, and what specific problem does your serverless data platform aim to solve?
Teams were hacking together brittle scripts to productionize LLM workflows, fighting rate limits, costs, retries, PII handling, and observability, just to get from prototype to a hardened production workflow. Typedef turns “inference as a one-off call” into a first-class data transform you can schedule, audit, and optimize like any other job. Our serverless platform removes infrastructure burden while giving strong guarantees on cost, latency, and lineage. We discovered a huge need to help teams operationalize their AI workloads.
3. How does Typedef's approach to processing AI workloads differ from traditional data processing methods?
Traditional ETL assumes deterministic transforms over structured data. AI workloads introduce probabilistic steps over unstructured inputs plus provider rate limits and token budgets. Typedef provides a declarative DataFrame-style API (via our OSS library, Fenic) and a managed runtime that handles batching, backoff, model routing, semantic pushdowns, and evaluation—so teams keep the simplicity of ETL with the power of LLMs.
4. Can you explain the concept of LLM-powered pipelines and how they benefit teams working with large volumes of unstructured data?
Think of a pipeline where steps include ingestion, chunking, classification, extraction, summarization, and enrichment—each with quality checks and budgets. Outputs are structured columns you can join back to your warehouse or data lake. Some of the benefits include reproducibility, observability, and cost control at scale, plus the ability to version prompts/models and roll back like any other data asset.
5. Your experience spans data center infrastructure, machine learning, and AI. How has this diverse background influenced your vision for Typedef?
Across data centers, ML platforms, and AI, I’ve noticed a repeating pattern: a powerful new primitive emerges, but adoption stalls until the right abstractions and infrastructure exist. With Typedef, I see LLM inference as that new primitive. My background helps me anticipate the inflection point and design the right scaffolding early.
6. What were some key lessons you learned from your time at Coolan and Salesforce that you've applied to your current venture?
At Coolan, I learned the importance of solving problems that are true painkillers rather than nice-to-have vitamins. That mindset has carried into everything I build. I also saw firsthand how critical it is to invest in observability and transparency from day one. alif teams can’t see what’s happening under the hood, they can’t trust or scale a system. My time at Salesforce reinforced the value of meeting enterprise requirements such as security, compliance, and auditability, while still keeping the product simple enough for quick adoption. Finally, both experiences showed me that when you get the core abstraction right, everything else—performance, usability, and reliability—naturally falls into place.
7. During your time at Tecton, you worked on feature platforms for machine learning. How has this experience shaped Typedef's product development?
Working on feature platforms at Tecton reinforced for me the value of treating data assets as declarative, versioned, and reproducible. Features had to work seamlessly across batch and streaming contexts, and strong lineage was critical for trust. Those principles directly shaped how we designed Typedef: prompts, schemas, and evaluations are all first-class, versioned assets, and pipelines are built as clear DAGs rather than ad hoc scripts.
At the same time, being dependent on a complex data processing platform like Spark made me realize how much friction heavy compute layers can create. Teams often spent more time managing infrastructure than focusing on the actual ML problem. That experience was a key motivator for building Typedef’s compute engine to be radically simpler—serverless, inference-aware, and optimized for the unique needs of LLM workloads. By stripping away unnecessary complexity, we give teams the ability to focus on building reliable pipelines instead of wrangling infrastructure.
8. In your opinion, what are the biggest challenges teams face when trying to derive insights from unstructured data like documents, transcripts, and logs?
The biggest challenge is that most teams end up writing large amounts of brittle glue code just to make unstructured data usable. They build ad hoc scripts for chunking, classification, extraction, and enrichment, and those scripts often rely on fragile assumptions about input formats or model behavior. While this approach may work for prototypes, it quickly breaks down when scaling into production—pipelines become hard to maintain, costs balloon, and quality becomes unpredictable. On top of that, every team reinvents the wheel for rate limiting, retries, and observability, which leads to wasted effort and inconsistent reliability. The result is that what should be straightforward use of AI for enrichment or analysis turns into a complex web of custom code that is expensive to scale and nearly impossible to govern.
9. How does Typedef aim to make AI-powered data analytics more accessible to teams without extensive infrastructure management expertise?
Typedef is designed to remove the heavy lifting that typically comes with building and maintaining AI-powered data pipelines. Instead of requiring teams to stitch together custom scripts, manage complex clusters, or tune infrastructure, we provide a fully serverless platform where the complexity is handled under the hood.
Teams write simple, declarative pipelines using our open-source library, Fenic, and Typedef takes care of the hard parts automatically—things like batching requests efficiently, handling retries and backoff under rate limits, routing across different model providers, and enforcing cost or latency budgets.
We also make governance and reliability accessible out of the box. Features like built-in evaluation, lineage tracking, and policy enforcement mean that teams can trust the outputs without needing a dedicated platform team. By combining a familiar DataFrame-style API with enterprise-grade runtime guarantees, Typedef makes AI-powered analytics approachable for data teams of any size, regardless of whether they have deep infrastructure expertise.
10. Can you share an example of how a typical customer might use Typedef to improve their data processing and analysis capabilities?
A typical example comes from a content company that needs to process thousands of articles daily. Their goal is to classify content by topic, extract semantic features such as tone, entities, or key concepts, and then feed those features into their recommendation and search systems. Historically, this would require writing and maintaining a large amount of custom code to handle everything from chunking text and calling models, to storing intermediate results and reconciling inconsistencies across different data sources. That code usually works for a prototype but becomes fragile and expensive to operate at production scale.
With Typedef, the company can express this entire workflow as a simple pipeline. They use semantic operators to classify articles, extract metadata, and generate embeddings for downstream recommendations. Typedef’s runtime takes care of batching, retries, rate limits, and budget enforcement, so the team can trust that the pipeline will run reliably at scale. The final output is a structured dataset of articles enriched with semantic features that can be joined back into their warehouse. This enables more accurate recommendations, improved editorial curation, and better SEO optimization—without the company needing to manage complex infrastructure or maintain brittle glue code.
11. What do you see as the most exciting developments in the field of AI and data analytics, and how is Typedef positioned to leverage these advancements?
One of the most exciting developments is the shift toward structured generation, where models are producing outputs that directly conform to schemas, making them far easier to integrate into real pipelines. At the same time, specialized smaller models are emerging that can deliver high accuracy at lower cost, and multimodal models are becoming practical for everyday use cases that span text, audio, and images. These advancements are pushing AI from experimental to truly operational. Typedef is positioned to take advantage of this evolution by providing a runtime that is model-agnostic, cost-aware, and built to handle inference as a native data operation. As new models and modalities become available, our platform allows teams to swap providers, optimize for quality or cost, and run their pipelines with predictable reliability—so they can benefit from the rapid pace of innovation without needing to rebuild their infrastructure each time.
12. As CTO of Typedef, what's your approach to building and scaling the engineering team, especially given your experience in this area at previous companies?
My approach to building the engineering team is to prioritize senior, product-minded engineers who not only write reliable and maintainable code but also think deeply about the customer experience. I believe the best teams are small, autonomous, and highly accountable, with clear ownership of components. What excites me most today is the opportunity to empower engineers with AI itself—using AI-assisted tools and workflows to make them dramatically more productive and efficient. By combining experienced builders with modern AI-driven development practices, we can move faster, maintain higher quality, and scale the platform without creating unnecessary overhead.
13. How do you envision the future of analytics, particularly in terms of integrating traditional data pipelines with modern AI workflows?
I believe the future of analytics will treat inference as just another transform in the data pipeline, supported by clean and reliable abstractions. Just as teams today use SQL or DataFrame operations to filter, join, and aggregate data, they will soon be able to apply semantic transforms—such as classification, summarization, or extraction—using the same familiar paradigms. These abstractions will handle the complexity of batching, retries, cost control, and evaluation behind the scenes, allowing data engineers to focus on outcomes rather than infrastructure. In this world, analytics platforms will seamlessly combine traditional structured operations with AI-powered inference, giving organizations a unified way to derive insights from both structured and unstructured data. Mixed AI workloads will become the norm.
14. What advice would you give to entrepreneurs looking to start a company in the AI and data analytics space?
Oh wow, lots of different learnings to share here. My advice is to focus on building a product that delivers real business value from day one, rather than chasing novelty or technology for its own sake. The fastest way to validate that value is by getting in front of customers early and starting go-to-market efforts on day one, even while the product is still evolving. Talking to users, testing pricing, and proving ROI are just as important as the technical vision. If you can anchor your product to measurable outcomes and build GTM muscle early, you’ll have a much clearer path to product–market fit and long-term success.
15. Looking ahead, what are your goals for Typedef in the next few years, and how do you plan to achieve them?
Our goal is to build the most durable and reliable infrastructure for AI and agentic workflows—one that allows teams to truly harness the benefits of AI at scale. Today, too many organizations are stuck in the gap between successful prototypes and production systems that can deliver real business value. Typedef’s mission is to close that gap by providing a platform where inference is as reliable and predictable as any other data operation. Over the next few years, we plan to deepen our integrations with warehouses and data lakes, expand the library of semantic operators, and continue to evolve our open-source project Fenic to deliver best-in-class developer ergonomics. By combining strong infrastructure foundations with abstractions purpose-built for AI, we aim to empower teams not only to deploy AI in production but to trust it as a core part of their analytics and decision-making stack.
As the AI and data analytics landscape continues to evolve rapidly, Typedef stands at the forefront of innovation, poised to reshape how organizations leverage AI for insights and decision-making. Yoni Michael's extensive experience and forward-thinking approach have culminated in a platform that not only solves current challenges in AI workflow management but also anticipates future needs in the field. With its focus on reliability, scalability, and ease of use, Typedef is well-positioned to become a cornerstone technology in the emerging era of AI-powered data processing and analytics.