fenic's Multiple Model Configuration & Selection lets you pick the right model for each step, cheap where you can, powerful where you must. Think of it as a per-operator model dial across your pipeline.

Fenic ❤️ Hugging Face Datasets! You can now turn any fenic snapshot into a shareable, versioned dataset on @hf.co perfect for reproducible agent contexts and data sandboxes. Docs: huggingface.co/docs/hub/dat...

"AI confidence is high — but production results still lag." Our cofounder, Yoni Michael, shares why in CIO. Read it here 👉 www.cio.com/article/4069... #CIO #AIinEnterprise #Typedef

fenic's Local Data Caching & Persistence keeps expensive AI steps from rerunning and your pipelines resilient.

fenic's Multiple Model Configuration & Selection lets you pick the right model for each step, cheap where you can, powerful where you must. Think of it as a per-operator model dial across your pipeline.

Why do most AI projects stall? Because going from prototype → production is HARD. On Data Exchange, we share how Typedef makes inference-first pipelines actually work at scale. 👉 thedataexchange.media/typedef-fenic/

We’re honored to be featured in AI World Today! 🚀 Our co-founder Yoni Michael shares how Typedef is closing the gap between AI prototypes and production, making inference a first-class data operation. 👉 Read the full interview: www.aiworldtoday.net/p/interview-...

We’re building the AI-native, inference-first infrastructure that powers scalable, production-ready LLM pipelines—no infrastructure headaches, just reliable results. Read more in AIM about how we’re overcoming pilot paralysis: aimmediahouse.com/ai-startups/...

Fenic brings the reliability of DataFrame pipelines to AI workloads—semantic joins, markdown parsing, transcripts, and more—now strengthened with the 0.3.0 update. Dive into the latest improvements. → www.techzine.eu/blogs/data-m...

AI fatigue is everywhere. But it’s not inevitable. In AI Journal, Typedef co-founder Yoni Michael shares how teams can escape “pilot paralysis” and move AI from prototype to production with confidence. 👉 Read the article: aijourn.com/ai-fatigue-i...

fenic's Structured Output Extraction turns LLM text into validated tables, directly in your DataFrame. Think of it as schema-first parsing: you define a Pydantic model; Fenic enforces it and returns structured columns.

fenic's First-Class AI Data Types make embeddings, markdown, and JSON real, typed columns, with the right operations built in. Think of it as strong types for meaning and structure: safer pipelines, richer queries.

fenic's Semantic Classification turns free-text into clean enums right inside your DataFrame.

fenic's Semantic Similarity Join (Vector Join) finds nearest neighbors across tables using embeddings, right inside your DataFrame.

fenic 0.4.0 is live: declarative tools for agents, a production-ready MCP server, and direct reads from HuggingFace plus big DX & reliability gains.  Highlights: Declarative tools: define function-calling tools as data (type-safe, reviewable, reusable). 

fenic ensures LLM outputs conform to schemas using Pydantic models.

fenic offers standard DataFrame operations with a familiar Spark/Pandas-like API.

fenic UDFs allow you to inject arbitrary Python (including external libraries) directly into the fenic execution plan while preserving lazy planning, metrics and reproducibility.

fenic offers Flexible Session Configuration. Define AI providers/models (OpenAI GPT‑4, Claude, etc.) and RPM/TPM rate limits at session start so the framework centrally manages throttling, costs and consistency across all AI calls.

Semantic Reduce = apply LLM aggregation at group-by scale. Many rows in → one summary per group. You write a prompt and fenic handles packing, ordering, and multi-pass reduction.

Kostas joins Ben on The Data Exchange Podcast, to unpack fenic’s approach to production-ready data processing.

Semantic Map is how you apply inference at scale with fenic. Whether it’s 1 call or 1M, it’s ~5 lines. You iterate on data and prompts; Fenic handles the rest. Write a Jinja template with column placeholders; Fenic renders it per row and calls the model.

AI-native data types in Fenic. in this demo: the Markdown type.

Fenic adds AI-native scalar functions to DataFrames. They run LLM inference over columns/rows, so reasoning lives inside your pipeline.

Semantic join is one of Fenic’s AI-native DataFrame functions. They operate over whole tables and relationships—not just individual rows.

Thanks to all how joined us for the Data Renegades happy hour during the #DataCouncil “No BS Data Conf” this week Did you try a "Look Before Merging" cocktail? We were joined by the best co-hosts ❤️ @tobikodata.com @bauplan.bsky.social @datacoves.com @typedef.ai See you all next year! #NoBSConf

Sometimes you just gotta process some unstructured data…

What an amazing night hosting some of the Peninsula’s brightest minds in data. Delicious tacos, amazing tech talks, and the 2025 view of data by Tomasz from TheoryVC!

The hardest thing about data infrastructure is that it exists. Infrastructure-less is such a 2025 vibe.

Tacos and data tech talks? Yes please! Join us and @tobikodata.com next week in our cozy San Mateo office! lu.ma/dm24afr0