Client Overview

Our client, a fast-growing U.S. fast-fashion retailer, operates 250 stores and a rapidly expanding e-commerce platform. Catering to Gen Z and Millennials, the brand offers affordable, trend-driven apparel with frequent launches and flash sales. As digital sales surged post-pandemic, they prioritized enhancing digital customer support for faster resolutions and 24/7 availability while keeping costs in check.

Challenge

High Support Volume

Avg. 15,000+ tickets/month, mostly about order status, return/refund tracking, and FAQs offers

Long wait times

Delays during peak periods like Black Friday and product launches.

Inconsistent quality

Human errors and knowledge gaps affecting support reliability.

Customer Churn Risk

Slow response times were affecting CSAT scores and repeat purchase rates

Limited Support Hours

No overnight or weekend coverage led to backlogs and lower CSAT

Rising Operational Costs

Scaling the team to meet demand increased staffing costs, training time, and resource planning complexity.

Objective

To implement a cost-efficient AI chatbot that :

Reduces support costs
Resolves at least 60% of common queries automatically
Offers instant, 24/7 support on the website and mobile app
Supports seamless human handover when needed
Maintains brand tone and improves customer satisfaction

Our Solution : AI-Powered Customer Support Platform

To streamline repetitive customer queries and enhance service scalability, we designed and deployed a multi-channel Generative AI chatbot, tightly integrated with the client’s existing systems and workflows. The solution was architected to support real-time interactions, personalized responses, and seamless handoffs to human agents when needed — all while maintaining brand voice and compliance standards.

Training & Knowledge Ingestion

The AI assistant was trained and optimized using a combination of :

Phase 1 : Historical Chat Logs

We ingested and vectorized insights from over 1 million past support interactions.
These logs helped identify common customer intents (e.g., order status, return requests, FAQ categories).
Cleaned and embedded using LangChain pipelines, then stored in FAISS for vector search.

Phase 2 : Structured Policies & Knowledge Base

Documents such as return and refund policies, shipping terms, and sizing charts were indexed using LangChain and chunked into embeddings.
The chatbot performs RAG (Retrieval-Augmented Generation) by combining this structured knowledge with GPT-4-turbo’s capabilities to reduce hallucinations and maintain factual correctness.

Phase 3 : Custom Prompts & Guardrails

System prompts and fallback handling were designed to ensure brand tone consistency.
Role-based prompt templates were created for guest users vs. logged-in customers.
Guardrails were added to prevent the AI from handling unsupported topics like payments or legal disputes, redirecting such queries to a human agent.

Core Functionalities

Order Tracking

Customers can ask, “Where is my order?” and the bot fetches real-time order status using REST API calls to the internal order database.

Refund Status

The bot retrieves refund processing status and expected timelines directly from the finance system through secure API access.

Return Policy Guidance

The bot checks return eligibility based on purchase date and product category by querying return policy rules stored in the system.

FAQs & Policy Explanation

Common questions (e.g., shipping delays, gift card usage, store hours) are answered using a hybrid of RAG (retrieval augmented generation) and document embedding via LangChain.

Live Agent Escalation

For edge cases, the chatbot escalates to a live agent and passes along full chat context and conversation history.

TechStack & Tools

OpenAI GPT-4-turbo

Powers natural and context-aware chatbot conversations.

LangChain

Enables prompt management and retrieval-augmented generation (RAG).

FAISS

Fast and lightweight vector database for embedded documents.

Node.js + Express

Efficient backend framework for APIs and chatbot logic.

PostgreSQL

Stores customer, order, return, and refund data securely.

Amazon API Gateway

Securely routes chatbot requests to internal services.

Amazon ECS

Deploys chatbot as a scalable containerized microservice.

Amazon S3

Stores static content and fallback assets.

Amazon CloudWatch

Monitors and logs chatbot and API performance.

Amazon Cognito

Manages user sessions and secure authentication.

React.js

Web-based chatbot UI integration.

Flutter

Cross-platform chatbot integration for mobile apps.

Team Composition

Project Manager: 1
Solution Architect: 1
Backend Developer: 1
Frontend Developer: 1
AI/Prompt Engineer: 1
QA Engineer: 1
Knowledge Trainer: 1

Business Impact & Results

$352,000+ annual direct savings in customer service labor costs

Easily handles query spikes during sales, product drops, or holiday seasons.

Reduced training & onboarding costs specifically during peak season.

24/7 customer support availability without extra headcount

Live agents now spend 70% more time on high-value conversations (e.g., complaints, escalations)

Better analysis & understanding of FAQ, common issues, and policy gaps.

Results Within 3 Months of Launch

Metric	Before (Manual Only)	After (With AI Chatbot)	Improvement
Monthly Queries Handled by Agents	15,000	6,000	✅ 60% offloaded to AI
Full-Time Agents Needed	12	5	✅ 58% workforce reduction
Avg. First Response Time	6.2 minutes	< 10 seconds	✅ 96% faster
Cost per Support Query	$2.05	$0.80 (blended)	✅ 61% cost reduction
Customer Satisfaction (CSAT)	82%	92%	✅ +10 point increase
Monthly Support Cost	$50,400	$21,000	✅ $29,400 saved/month
Annual Support Cost	$604,800	$252,000	✅ $352,800 saved/year

Testimonials

Our Slam Book

Tony Lehtimaki

DIRECTOR - AMEOS

Spain

Very professional, accurate and efficient team despite all the changes I had them do. I look forward to working with them again.

Antoine de Bausset

CEO - BEESPOKE

France

They are great at what they do. Very easy to communicate with and they came through faster than I hoped. They delivered everything I wanted and more! I will certainly use them again!

Vivek Singh

MARKETING & SALES HEAD - VARMORA

Gujarat

I really liked their attention to detail and their sheer will to do the job at hand as good as possible beyond professional boundaries.

Nimesh Patel

DIRECTOR - COVERTEK CERAMICA

Gujarat

Excellent work, and on time with all goals. Communication was very easy, and knowledge of work was excellent. Will be working with them on upcoming projects. I highly recommend.

Craig Zappa

DIRECTOR - ENA PARAMUS

United States

"I would like to recommend their name to one and all. No doubt" their web app development services cater to all needs.

Neil Lockwood

CO-FOUNDER - ESR

Australia

Aglowid is doing a great job in the field of web app development. I am truly satisfied with their quality of service.

Daphne Christoforidou

CEO - ELEMENTIA