Agentic Marketplace

Agentic Marketplace

Author: Demetrios March 20, 2026 Duration: 51:26

Donné Stevenson is a Machine Learning Engineer at Prosus, working on scalable ML infrastructure and productionizing GenAI systems across portfolio companies.


Pedro Chaves is a Data Science Manager at OLX Group, working on GenAI-powered search, personalization, and large-scale marketplace recommendations.


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// Abstract

Marketplaces are about to get smarter.Agents that find your perfect house, negotiate the best deals, and even talk to other agents on your behalf.


Less tedious searching. Less back-and-forth. More time for what matters.


Pedro Chaves and Donné Stevenson discuss the future of buying and selling cars, homes, and everything in between - and what it'll take to get there.


// Bio

Donné Stevenson

Focused on building AI-powered products that give companies the tools and expertise needed to harness the power of AI in their respective fields.


Pedro Chaves

Pedro is a Data Science Manager at OLX Group, where he leads teams building machine learning solutions to improve marketplace performance, pricing, and user experience at scale.


// Related Links

Website: https://www.prosus.com/

Website: https://www.olxgroup.com/


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Timestamps:

[00:00] OLX: Disrupting Buyer-Seller Experiences

[03:33] Redefining the Home-Buying Experience

[07:40] User Feedback and Iterative Rollouts

[11:25] Beyond Chat: Redefining Agent Use

[14:03] User Trust and Education Challenges

[16:47] Learning Curve for Automoto

[20:05] Interactive Decision-Making with AI

[24:47] Agents Simplify Buyer-Seller Search

[28:14] Garage Sale Treasure Hunting

[33:43] Agent Discovery Layer Needed

[34:53] Agents Relying on Agents

[39:48] Reducing Friction in Selling Stuff

[41:39] Extracting Buyer Intent Systematically

[44:49] Optimizing Delivery with Lockers

[50:10] Generative AI Commerce Strategies

[51:03] Improving Chat Interaction Layer


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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