{"ID":2850911,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20099","arxiv_id":"2510.20099","title":"AI PB: A Grounded Generative Agent for Personalized Investment Insights","abstract":"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.","short_abstract":"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 bet...","url_abs":"https://arxiv.org/abs/2510.20099","url_pdf":"https://arxiv.org/pdf/2510.20099v1","authors":"[\"Daewoo Park\",\"Suho Park\",\"Inseok Hong\",\"Hanwool Lee\",\"Junkyu Park\",\"Sangjun Lee\",\"Jeongman An\",\"Hyunbin Loh\"]","published":"2025-10-23T00:51:59Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CE\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}