Typedef Launches New Release of Open-Source Project Fenic – a DataFrame Purpose-Built for LLM Inference

Helping AI and Data Teams Build Faster, Cheaper, and More Reliable AI Pipelines for Production at Scale

SAN MATEO , CA, UNITED STATES, August 21, 2025 /EINPresswire.com/ -- Typedef Inc., turning AI prototypes into scalable, production-ready workloads that generate immediate business value, today announced a new release of its open-source project Fenic, a PySpark-inspired DataFrame for building AI and agentic applications.

Fenic enables AI and data engineering teams to transform unstructured and structured data into insights using familiar DataFrame operations enhanced with semantic intelligence. It features first-class support for markdown, transcripts, and semantic operators, coupled with efficient batch inference across any model provider.

New features found in Fenic version 0.3.0 include:
● Rust-powered Jinja templating as a first-class column function for dynamic, data-aware prompts (loops, conditionals, arrays).
● Built-in fuzzy string matching with 3 comparison modes and 6 algorithms for blocking, deduping, and record linkage, before you spend tokens.
● Pydantic-driven schemas across semantic operators for clean, typed outputs (including semantic.map with structured return types).
● Persistent views to save, reuse, and compose pipelines from your Fenic catalog.
● New functions & models (e.g., Cohere & Gemini embeddings, summarization) plus meaningful performance & DX improvements.

“Typedef is fully committed to accelerating innovation and time-to-value by building in the open,” said Kostas Pardalis, Co-Founder of Typedef and Fenic Steward. “The latest release of Fenic features changes that will result in a lot less glue code, fewer brittle prompts, and cheaper, more reliable pipelines – helping ship AI workflows to production faster.”

Unlike traditional data tools retrofitted for Large Language Models (LLMs), the Fenic query engine is built to work with AI models and unstructured data like emails and call transcripts. Other popular use-cases built with Fenic include: semantic feature engineering for recommended system models; high-precision named entity recognition and duplication; automated user-generated content moderation; and transaction enrichment and classification for fintech firms.

While the open Fenic provides a powerful foundation to build AI pipelines with semantic intelligence baked in, Typedef supercharges it with commercial features to make it more scalable across an enterprise. The startup that launched in June 2025, offers support for more complex, mixed AI workflows; collaboration via a web-based user interface; and reporting and analytics. Moreover, Typedef allows for rapid, iterative prompt and pipeline experimentation to quickly determine production-ready workloads that will demonstrate value. These capabilities arm data teams with the ability to easily integrate LLM inference into their analytics pipelines.

Gartner predicts that more than 40 percent of agentic AI projects will be canceled or fail by the end of 2027 due to escalating costs and unclear business value. Pilot paralysis is a well-documented epidemic affecting the bulk of enterprise AI projects with some research estimating the failure to scale rate to be as much as 87 percent. Typedef is looking to right this wrong by structuring unstructured data, operationalizing inference, and unlocking semantic insight at scale.

To learn more about the new features in Fenic, visit the blog post and the GitHub project page for full documentation and to get started.

About Typedef Inc.
Typedef enables organizations to drive new levels of analytic insight and competitive advantage from AI initiatives – moving AI projects from pilot to production faster, more efficiently, and at scale. The purpose-built, AI data infrastructure for modern workloads handles LLM-powered pipelines, unstructured data processing, inference complexity, and the running of batch AI workloads in production, allowing data and AI teams to focus less on managing complex infrastructure and more on driving innovation and business value – fully realizing the promise of AI. Typedef has made a significant portion of its technology open source, available on GitHub as Fenic. To learn more about Typedef or to demo the product, visit www.typedef.ai

April Burghardt
Typedef
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