typedef
@typedef.ai
We are here to eat bamba and revolutionize the world of query engines. The Spark is gone, let's rethink data processing with a pinch of AI
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.
Think of it as a per-operator model dial across your pipeline.
October 21, 2025 at 11:07 PM
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.
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...
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...
fenic
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
October 21, 2025 at 6:57 PM
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...
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
Our cofounder, Yoni Michael, shares why in CIO.
Read it here 👉 www.cio.com/article/4069...
#CIO #AIinEnterprise #Typedef
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.
www.cio.com
October 20, 2025 at 11:07 PM
"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
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.
September 24, 2025 at 1:38 AM
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.
Think of it as a per-operator model dial across your pipeline.
September 22, 2025 at 11:07 PM
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.
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/
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/
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.
thedataexchange.media
September 21, 2025 at 12:05 AM
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/
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-...
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-...
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.
www.aiworldtoday.net
September 20, 2025 at 9:36 PM
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-...
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/...
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.
aimmediahouse.com
September 20, 2025 at 8:42 PM
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...
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,…
www.techzine.eu
September 20, 2025 at 3:57 PM
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...
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...
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
aijourn.com
September 20, 2025 at 2:44 AM
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...
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.
Think of it as schema-first parsing: you define a Pydantic model; Fenic enforces it and returns structured columns.
September 20, 2025 at 1:38 AM
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.
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.
Think of it as strong types for meaning and structure: safer pipelines, richer queries.
September 18, 2025 at 1:38 AM
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.
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.
September 16, 2025 at 1:38 AM
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.
September 12, 2025 at 2:26 AM
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).

Highlights:
Declarative tools: define function-calling tools as data (type-safe, reviewable, reusable).

September 9, 2025 at 9:20 PM
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).

Highlights:
Declarative tools: define function-calling tools as data (type-safe, reviewable, reusable).

fenic ensures LLM outputs conform to schemas using Pydantic models.
September 8, 2025 at 5:45 PM
fenic ensures LLM outputs conform to schemas using Pydantic models.
fenic offers standard DataFrame operations with a familiar Spark/Pandas-like API.
September 6, 2025 at 2:44 AM
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.
September 5, 2025 at 2:44 AM
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.
September 3, 2025 at 3:57 PM
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.
August 29, 2025 at 2:44 AM
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.
August 27, 2025 at 1:38 AM
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.
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.
August 25, 2025 at 11:07 PM
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.
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.
August 21, 2025 at 5:02 PM
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.
August 20, 2025 at 6:45 PM
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.
August 20, 2025 at 1:46 AM
Semantic join is one of Fenic’s AI-native DataFrame functions. They operate over whole tables and relationships—not just individual rows.