We present AI PB, a production-scale generative agent deployed in real retail finance. Unlike reactive chatbots that answer queries passively, AI PB proactively generates grounded, compliant, and user-specific investment insights. It integrates (i) a component-based orchestration layer that deterministically routes between internal and external LLMs based on data sensitivity, (ii) a hybrid retrieval pipeline using OpenSearch and the finance-domain embedding model, and (iii) a multi-stage recommendation mechanism combining rule heuristics, sequential behavioral modeling, and contextual bandits. Operating fully on-premises under Korean financial regulations, the system employs Docker Swarm and vLLM across 24 X NVIDIA H100 GPUs. Through human QA and system metrics, we demonstrate that grounded generation with explicit routing and layered safety can deliver trustworthy AI insights in high-stakes finance.
@article{arxiv.2510.20099,
title = {AI PB: A Grounded Generative Agent for Personalized Investment Insights},
author = {Daewoo Park and Suho Park and Inseok Hong and Hanwool Lee and Junkyu Park and Sangjun Lee and Jeongman An and Hyunbin Loh},
journal= {arXiv preprint arXiv:2510.20099},
year = {2025}
}