---
title: "Beyond Bots: Nare Vardanyan on Data Enrichment & AI (Ntropy)"
url: https://stacklist.com/card/d5eeb849-4ad4-48b4-81f2-166a9981adc1
source_url: "https://www.codestory.co/episodes/beyond-bots-nare-vardanyan-ntropy/"
stack: https://stacklist.com/c/podcast/stack/d65e0d70-c465-450c-bdab-47e8de98de94
summary: "Code Story podcast features Ntropy CEO Nare Vardanyan in the \"Beyond Bots\" series, discussing large language models, deep learning, and the real impact of AI on financial services. The episode covers topics including model training, data standardization, latency, and Ntropy's approach to financial data enrichment using LLMs."
tags: "large-language-models, financial-services, ai, fintech, data-enrichment, startup, podcast"
key_entities: "Nare Vardanyan (person), Ntropy (organization), Code Story (organization), Large Language Models (technology), Financial Data Enrichment (concept), Deep Learning (concept), Cash App (technology), Plaud AI (organization)"
classification: "transcript"
content_hash: "sha256:5b6794c6df7268fa0b047f764e8f3e59e52bea7688da70e26a41c9d234e0794b"
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token_counts_approximate: 441
visibility: public
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status: "final"
---

# Beyond Bots: Nare Vardanyan on Data Enrichment & AI (Ntropy)

Code Story: Insights from Startup Tech Leaders — Beyond Bots - Naré Vardanyan, Ntropy Listen to Episode Beyond Bots - Naré Vardanyan, Ntropy Code Story: Insights from Startup Tech Leaders 0:00 19:11 1× Today we are kicking off a special and new 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: Naré Vardanyan , CEO & Co-founder of Ntropy Questions: Tell me what a large language model is. Is there any difference between this and deep learning models? What is the difference between language models and LLM's? What sort of data sets were your models pre-trained on? How long does that take? How does the cost of training these models factor into your offering? Can you talk about latency as a key factor in this process? You've told me Ntropy has been doing this since the beginning, but give me a little more - how does this fit into Ntropy's approach? What is in the future for Ntropy in this space, specifically as it relates to LLM's and financial services? Links Website: https://www.ntropy.com/ LinkedIn: https://www.linkedin.com/in/narevardanyan/ 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