REALMS: An AI-Assistant Conversational System for Real-Time Exact Audience Sizing over High-Dimensional Nested Profiles

Researchers introduced REALMS (Real-time Exact Audience sizing via LLM-based Multi-attribute Search), a conversational system that returns exact audience counts in seconds from profile stores holding millions of profiles and thousands of attributes, according to an arXiv paper (arXiv:2609.30547v1). REALMS combines embedding-based vector search for categorical attribute retrieval, an LLM-powered NL2SQL pipeline with template-based in-context learning for nested schemas, and schema standardization for industry-agnostic deployment, and is deployed in production on an enterprise customer data platform. Evaluation on real enterprise data showed strong attribute-retrieval recall, high SQL execution accuracy and low latency, replacing prior methods that required hours.

arXiv:2609.30547v1 Announce Type: new Abstract: Audience sizing is a critical component of digital marketing. It enables precise resource allocation, campaign planning, and performance optimization. Traditional approaches using skeleton audiences, sampling, or predictive modeling suffer from significant delays, estimation errors, and poor scalability over high-dimensional profile data. We present REALMS Real-time Exact Audience sizing via LLM-based Multi-attribute Search , a conversational system for exact audience sizing deployed in production on an enterprise customer data platform. REALMS enables marketers to query massive profile stores with millions of profiles and thousands of attributes using natural language and receive precise counts in seconds. The system introduces three key components: 1 a categorical attribute retrieval mechanism using embedding-based vector search to dynamically identify relevant schema attributes without manual configuration; 2 an LLM-powered NL2SQL pipeline with template-based in-context learning for accurate query generation over complex nested schemas; and 3 schema standardization enabling industry-agnostic deployment across diverse enterprise environments. Evaluation on real enterprise data demonstrates strong recall for attribute retrieval, high SQL execution accuracy, and low latency, which enables real-time interactive audience insights where prior methods required hours.