In this episode of Beginner’s Guide to AI, we sit down with Alex Kihm, founder of POMA AI, to explore how enterprises can finally make sense of their data. AI search is broken, RAG often fails, and corporate documents are notoriously hard for LLMs to interpret. Alex explains how POMA AI’s patented method reconstructs structure inside unstructured data, enabling powerful, accurate enterprise search.

You’ll hear how his journey from engineering to legal tech to big-data econometrics led to a breakthrough in information structuring. Alex shares why PDFs confuse AI systems, how chunking destroys meaning, and why context engines will replace classical retrieval systems. This is a deep, funny, insightful conversation about what AI can and cannot do — and how companies can use it responsibly.

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About Dietmar Fischer

Dietmar is a podcaster and AI marketer from Berlin. If you want to elevate your AI strategy or your digital marketing, feel free to reach out anytime at Argoberlin.com

Quotes from the Episode

“Chunking is like reading wrongly sorted text messages from the 90s.”
“Intelligence is pattern recognition — and most enterprise data is not recognisable to machines.”
“PDF was made for printers, not for AI.”
“POMA AI restores the spatial awareness inside documents — the missing context that LLMs need.”
“We don’t do RAG anymore. We build context engines.”
“If your AI breaks the world, show me the invoice.”

Chapters

00:00 Welcome and Introduction

02:45 Alex Kihm’s Background: Engineering, Legal Tech and Early AI Work

10:32 The Problem with RAG, Training, Fine-Tuning and Hallucinations

18:55 The Birth of POMA AI and Solving the Chunking Problem

32:40 How POMA AI Rebuilds Document Structure and Enables True Enterprise Search

45:50 AI Safety, Manipulation Bots and The Future of AI in Business

52:10 Where to Find Alex Kihm and Closing Thoughts

Where to Find the Dr. Alex Kihm

All you need to know you'll find on the website: poma-ai.com
Contact Alex on LinkedIn:

Music credit: "Modern Situations" by Unicorn Heads

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