---
title: "Beyond Bots: Ilia Zintchenko on Transaction Classification AI (Ntropy)"
url: https://stacklist.com/card/223bfd0a-2d95-421f-bcb4-8b484f4747b1
source_url: "https://www.codestory.co/episodes/beyond-bots-ilia-zintchenko-ntropy/"
stack: https://stacklist.com/c/podcast/stack/d65e0d70-c465-450c-bdab-47e8de98de94
summary: "Code Story podcast features a special episode with Ilia Zintchenko, CTO and Co-founder of Ntropy, discussing their LLM stack, reliability optimization, and the use of small and large language models for financial data standardization. The conversation covers system costs, data sources, predictive vs generative learning, and the real impact of AI on financial services."
tags: "llm, financial-data, ai, startup, podcast, fintech, data-enrichment"
key_entities: "Ilia Zintchenko (person), Ntropy (organization), Code Story Podcast - Beyond Bots (event), LLM (technology), financial data standardization (concept), predictive vs generative learning (concept), Nare (person)"
classification: "transcript"
content_hash: "sha256:fbd4b09e35ba4f32a60ec8191c83bf2ad14dff1c98facb9c3401995c75693719"
acp_version: "0.2"
token_counts_approximate: 480
visibility: public
agent_accessible: true
status: "final"
---

# Beyond Bots: Ilia Zintchenko on Transaction Classification AI (Ntropy)

Code Story: Insights from Startup Tech Leaders — Beyond Bots - Ilia Zintchenko, Ntropy Listen to Episode Beyond Bots - Ilia Zintchenko, Ntropy Code Story: Insights from Startup Tech Leaders 0:00 19:22 1× Today we are dropping another special episode of the Code Story podcast, as part of our series entailed Beyond Bots: the REAL impact of AI on financial services, brought to you by our friends at Ntropy . As a reminder, Ntropy is the most accurate financial data standardization and enrichment API. They can take in any data source, any geography, and understand / enrich a financial transition in milliseconds. Made for developers, for fast, easy implementation. Check out their product at Ntropy.com . Guest: Ilia Zintchenko , CTO & Co-founder of Ntropy Questions: We talked with Nare about Ntropy and LLM's. But let's dig in more.... what is your LLM stack? How did you choose it, what were the considerations? What are the system costs in doing this? How do you optimize on reliability - what sort of lever are you pulling to ensure reliability?' How are you thinking about predictive vs generative learning? You guys have been using small and large LM's since the beginning - why is this significant? What data sources have you been using, and are there some that are better than others?' Have you had to scrub these data sources in any way to prep them for your LM? What is the major benefit that Ntropy is providing by using LLM? What would you go back and change if you could? Links Website: https://www.ntropy.com/ LinkedIn: https://www.linkedin.com/in/iliazintchenko/ Our Sponsors: * Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app * Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.ai Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy Latest Season of the Podcast is Sponsored By