GitHub - typedef-ai/fenic: Build reliable AI and agentic applications with DataFrames Build reliable AI and agentic applications with DataFrames - typedef-ai/fenic

Note: auto-routing is being explored; today you keep full control.
check the repo for more: github.com/typedef-ai/f...

Mix providers (OpenAI, Anthropic) with simple aliases

Use defaults for simple ops; override model_alias for complex ones

Balance cost/latency/quality without extra orchestration

Teams often wire a single model and pay in either cost or quality.

With Fenic, you register multiple models once and select them per call.

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.

Thanks to @danielvanstrien.bsky.social and @lhoestq.hf.co for the collaboration and feedback that made this possible and to David Youngworth you built and maintains the integration!

fenic We’re on a journey to advance and democratize artificial intelligence through open source and open science.

A few things you can do with this new integration.

1. Rehydrate the same agent context anywhere (local → prod)
2. Versioned, auditable datasets for experiments & benchmarks

fenic We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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...

CIOs’ AI confidence yet to match results While a large percentage of IT and business leaders believe their AI efforts will meet or exceed expectations, only a small number have successfully deployed projects thus far.

"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

Common patterns: multi-step enrichment, RAG prep, nightly jobs with partial recomputes.

for more check the Github repo: github.com/typedef-ai/f...

With fenic, it’s explicit and simple: call .cache() where it matters.

Protect pricey semantic ops (classify/extract) from re-execution

Reuse cached results across multiple downstream analyses

Recover from mid-pipeline failures without starting over

Think of it as checkpointing for LLM workloads: cache after costly ops, restart from there if something fails.

Without caching, teams re-pay tokens and time on retries: flaky APIs, disk hiccups, long recomputes.

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

Mix providers (OpenAI, Anthropic) with simple aliases

Use defaults for simple ops; override model_alias for complex ones

Balance cost/latency/quality without extra orchestration

Teams often wire a single model and pay in either cost or quality.

With Fenic, you register multiple models once and select them per call.

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.

The Fenic Approach to Production-Ready Data Processing Kostas Pardalis on Inference-First Data Frames, Markdown as Structure, Semantic Query Operations, and Production AI Debugging.

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/

Bridging the AI Gap: How Yoni Iny's Typedef is Revolutionizing Data Processing Yoni Michael, tech veteran and Typedef co-founder, transforms AI-powered data analytics with an innovative serverless platform for LLM workflows.

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-...

For AI to Scale, Infrastructure Has to Change-Typedef Gets It Typedef, a new AI infrastructure startup that officially launched on June 18, 2025, raised $5.5 million in seed funding, led by Pear VC.

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/...

Typedef project Fenic: A ‘dataframe’ for LLMs Typedef provides purpose-built AI data infrastructure services for cloud workloads that need to handle LLM-powered pipelines, unstructured data Typedef  is Helping AI and Data Teams Build Faster,…

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 Real, But It's Fixable | The AI Journal Enterprises have embraced generative AI with high expectations – new business insights, automated agents, real-time decision-making. What many got instead are

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...

Common patterns: review mining, invoice parsing, lead enrichment, spec extraction.

for more, check the GitHub repo: github.com/typedef-ai/f...

Define a Pydantic schema; get type-checked structs (ints, bools, lists, Optionals)

Auto-prompting via function calling / structured outputs (OpenAI, Anthropic)

Use unnest() and explode() to work with the data—no manual JSON wrangling

Most teams hand-roll JSON parsing, brittle regex, and post-hoc validators. That’s slow and error-prone.

With fenic, you keep it declarative.

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.

Common patterns: doc mining, content ingestion, RAG prep, taxonomy extraction.

for more, including examples and documentation, check: github.com/typedef-ai/f...

Type safety: Embedding/Markdown/JSON columns prevent incompatible ops
Built-ins that matter: normalize, similarity, jq queries for JSON and many more
Less glue: query structured + unstructured togethe,; mix dataframes + SQL + AI in one plan

Most teams treat AI artifacts as loose strings/arrays: schema drift, brittle casting, ad-hoc JSON parsing, and inconsistent similarity math.

In fenic, these are first-class.

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.

for examples and more information, check: github.com/typedef-ai/f...

Most teams fight drift: regex stacks, ad-hoc prompts, inconsistent tags.

With fenic you can:

Define classes once; get schema-clean, consistent labels

Zero-shot or few-shot with real examples (not just descriptions)

Batching, caching, retries built-in all testable in the same plan.