AlgoPlex

The Problem

As LLMs become the interface through which users consume information, existing ad-ranking models designed for search engines and feeds become obsolete. There was no established mechanism for ranking and delivering advertisements within LLM-generated responses in a way that was relevant, non-deceptive, and economically viable. Stanford researcher Prof. Michael Ostrovsky needed a technical platform to prototype and evaluate candidate mechanisms.

The Solution

We built AlgoPlex as a research infrastructure platform that enables rapid prototyping and evaluation of ad-ranking algorithms in LLM content pipelines. The system provides a sandboxed environment where different ranking mechanisms can be tested with real LLM outputs, measuring relevance, user experience, and economic metrics. This created the technical foundation for publishable research on a genuinely novel problem at the intersection of mechanism design and AI.

Tech Stack