CoutureConnect: An Adaptive AI Agent for Boutique Retail Automation

Abstract

CoutureConnect is an Adaptive AI Agent prototype designed for small boutiques to transform retail engagement and streamline operational workflows by addressing the core challenges of inconsistent digital presence, delayed customer communication, and the absence of structured customer records. The system integrates an Adaptive Content Engine that generates Instagram and WhatsApp-ready social media drafts while continuously learning from past engagement data to refine content strategies, alongside an Agentic Customer Flow that automates personalized customer notifications for order updates and new product launches while maintaining evolving customer profiles containing sizes, style preferences, and spend patterns. By proactively executing tasks based on internal triggers such as order completion and product releases, CoutureConnect delivers measurable improvements in time savings, engagement uplift, and notification coverage, offering a scalable and sustainable growth enabler for boutique retailers operating without dedicated marketing or CRM infrastructure.

Introduction

Small boutique retailers struggle to compete in the digital marketplace because they lack the marketing resources, CRM systems, and automation tools available to large retail companies. Managing inventory, customer communication, social media marketing, and personalized engagement simultaneously is difficult for small business owners. Existing tools are often fragmented, with separate solutions for content creation, CRM, and messaging, making them unsuitable for boutique-scale operations.

CoutureConnect is proposed as an integrated AI-powered retail ecosystem that combines an Adaptive Content Engine, Agentic Customer Notification System, and centralized customer management. The platform uses customer profiles, purchase history, and engagement data to automatically generate personalized Instagram and WhatsApp campaigns while sending automated notifications during order updates, product launches, and customer re-engagement activities.

The system addresses the limitations of existing enterprise-focused AI and CRM solutions by providing a lightweight, scalable platform designed specifically for boutique retailers.

Methodology

The system follows a design science approach involving problem identification, system development, and prototype evaluation. It is developed using:

• Frontend: React.js, Vite, Tailwind CSS, Recharts
• Backend: FastAPI with Python
• Database: Supabase (PostgreSQL)
• AI Modules: Adaptive content generation and rule-based fallback systems

Customer information such as purchase history, preferences, sizes, and engagement patterns is stored centrally. The system applies:

• RFM (Recency, Frequency, Monetary) analysis for customer segmentation into VIP, Regular, and At-Risk groups.
• CLV scoring to estimate customer value.
• Engagement analysis to improve future marketing campaigns.

System Architecture

CoutureConnect consists of:

• React-based dashboard: Allows boutique owners to manage content, customers, and orders.
• Agent-based workflow controller: Automatically handles business events and triggers.
• Five core modules:
  1. AI Content Generation
  2. Notification Draft Generation
  3. Adaptive Preference Engine
  4. Order Management
  5. Customer Profile Management

All modules share a single database, allowing customer interactions, purchases, and campaign performance to continuously improve personalization.

Results and Performance

The prototype was tested with:

• 58 customers
• 147 orders
• 140 automated trigger events
• 5 weeks of operation

Key outcomes:

• Reduced weekly manual workload from 8–10 hours to about 1.2 hours, achieving approximately 85% time savings.
• Automated order notifications achieved 100% coverage.
• Overall notification success rate reached 97.9% with an average response time of 165 ms.
• Adaptive AI content generation improved engagement from 12.4% to 19.8%, resulting in around 60% engagement uplift.
• Customer segmentation identified:
  • VIP customers: 24%
  • Regular customers: 53%
  • At-Risk customers: 22%

Conclusion

This paper presents CoutureConnect, a modular AI-based system designed to support small boutique retailers by automating key operational tasks. The system combines an Adaptive Content Engine with an Agentic Customer Flow, both connected through a shared customer data layer, allowing content generation, communication, and customer management to work together seamlessly. The evaluation showed a clear improvement in efficiency and engagement. The system reduced weekly manual effort by around 80–85%, improved content engagement across iterations, and achieved high automation coverage (~98%) for notification workflows. While these results are based on simulated data, they demonstrate the practical potential of the system in real-world boutique environments. More importantly, the system highlights that boutique businesses do not require complex or enterprise-level tools. Instead, they benefit from simple, integrated solutions that match their daily workflows, reduce repetitive tasks, and improve consistency in customer interaction. CoutureConnect shows that such a system is both feasible and effective, even at a small operational scale. Future work will focus on real-world deployment and further improving adaptability and personalization through advanced learning mechanisms.

References

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Copyright

Copyright © 2026 Pothani Akshaya Bhavani, Pravalika Gande, Tanvi Kovai, Saachi Manish Bhalgat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.