Skip to content
Log in
#artificial intelligence Preprint

Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement

Mandar Kulkarni A. Pooja Samir Shah
Aug 2026 · 0 citations · 10 references
Computer Science

TL;DR

A scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization is presented.

Abstract

Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for exploratory intents such as best smartphones or latest 5G phones, where users may leave the platform for external research before purchasing. We present a scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents. The system identifies users with exploratory purchase intent and low engagement, conducts grounded multi-agent product research using behavioral signals, external knowledge, and enterprise catalog data, and delivers personalized recommendations through WhatsApp. We evaluate the framework in a 23-day production deployment involving approximately 15K WhatsApp notifications for mobile product discovery. The campaign achieved substantial CTR improvements over traditional WhatsApp recommendation campaigns, with evidence of secondary engagement through message forwarding and sharing. The deployment also generated downstream purchases and GMV impact, demonstrating the practical effectiveness of AI Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization.

arXiv Semantic Scholar
View source

Similar papers

#artificial intelligence Review Dec 2025

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.

Ruanqianqian Huang, Avery Reyna, Sorin Lerner et al. · 19 citations · ⚡1
#artificial intelligence Open access Oct 2022

Adaptive surrogate modeling for high-dimensional spatio-temporal output

An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.

B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al. · 17 citations
#artificial intelligence Preprint Feb 2025

`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs

An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.

Linghan Huang, Bo Li, Huaming Chen et al. · 12 citations · ⚡2
#artificial intelligence Review Open access Oct 2025

Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead

This review provides a systematic literature review of LLM-based Verilog code generation, analyzing 102 papers (70 published and 32 high-quality preprints) from SE, AI, and EDA venues and outlines a roadmap highlighting potential opportunities in LLM-assisted hardware design.

Guang Yang, Wei Zheng, Xiang Chen et al. · 11 citations · ⚡1
#artificial intelligence Apr 2026

From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?

This work introduces Behavior-Outcome Freedom (F), a pre-synthesis diagnostic of signed behavior-outcome rank mismatch, and formalizes its candidate-conditional role through Signed Anchor-Rank Transfer, which preserves validated capability resources, removes runtime orchestration, and conditionally inherits pipeline guidance using a calibrated rule over F.

Binyan Xu, Dong Fang, Haitao Li et al. · 10 citations
#artificial intelligence Jan 2025

Diffusion Models for Smarter UAVs: Decision-Making and Modeling

Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.

Yousef Emami, Hao Zhou, Luís Almeida et al. · 9 citations

Related blog posts