Idea
Conversational AI assistant for C2C marketplaces that automates seller workflows and improves buyer product discovery.
Research Paper
Core Innovation
This paper introduces FaMA, an AI assistant leveraging large language models to convert traditional GUI-based C2C marketplace tasks into conversational workflows. It uniquely automates complex seller operations and enhances buyer search experiences, achieving high task success and faster interactions. This approach differs from prior work by integrating agentic AI capabilities specifically tailored for C2C marketplaces.
Market Size (TAM)
$10–20B TAM, $2–5B SAM; assumption: large global C2C e-commerce market with growing AI adoption in user experience enhancement.
Potential Customers & Pain Points
- C2C Marketplace Sellers Facing High-Friction Bulk Messaging and Listing Updates
- Buyers Struggling with Inefficient Product Discovery
- Marketplace Platforms Seeking Lightweight Conversational Interfaces
Business Model
SaaS subscription for marketplace platforms plus usage-based fees for AI-powered automation features.
Competitive Landscape
- Letgo
- OfferUp
- Facebook Marketplace
Implementation Challenges
- Integration with diverse marketplace platforms
- User trust in AI-driven automation
- Handling varied and complex user intents
Validation Strategy
- Pilot integration with select C2C marketplaces
- Measure task success rate and interaction speed improvements
- Collect user feedback on conversational experience
Research Paper Overview
FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace
Summary
This paper presents FaMA, an AI assistant powered by large language models that transforms complex C2C marketplace interactions from GUI-based to conversational AI. FaMA automates high-friction workflows for sellers like bulk messaging and listing updates, and enhances buyers' product discovery via conversational search. The architecture enables a lightweight, efficient, and accessible marketplace experience, achieving 98% task success and up to 2x faster interactions.