Challenge
In the hypercompetitive world of eCommerce, one of the biggest hurdles is user experience. Online shoppers often struggle to find the products that truly fit their needs, especially when catalog sizes scale into thousands of SKUs. This results in customer frustration, increased bounce rates, and decreased conversion. The traditional approach of search bars and filter systems isn’t enough anymore—consumers now expect personalized and conversational experiences that mirror human assistance.
Our client came to us with a clear objective: build an AI-powered product discovery engine that simulates an intelligent conversation with a human assistant. The tool had to be capable of understanding natural language queries and intelligently matching those inputs with relevant products from an eCommerce site’s catalog. The primary deployment would be on websites running Shopify or WordPress, but the client also envisioned future extensions for messaging platforms like WhatsApp and Discord.
The project was ambitious. We needed to not only integrate a powerful AI model but also ensure smooth interaction with different CMS and messaging platforms, while maintaining data privacy, performance, and user trust. Another significant challenge was helping the AI understand context—users don’t always provide exact product names, so the bot needed to interpret vague queries and still deliver relevant results.
Finally, the solution needed to offer a seamless integration path for non-technical eCommerce site owners, meaning a lightweight plugin or API-ready system that wouldn’t require deep customization to install and deploy. This meant everything from backend design to UX had to be streamlined, intuitive, and scalable across different industries and product categories.
In the hypercompetitive world of eCommerce, one of the biggest hurdles is user experience. Online shoppers often struggle to find the products that truly fit their needs, especially when catalog sizes scale into thousands of SKUs. This results in customer frustration, increased bounce rates, and decreased conversion. The traditional approach of search bars and filter systems isn’t enough anymore—consumers now expect personalized and conversational experiences that mirror human assistance.
Our client came to us with a clear objective: build an AI-powered product discovery engine that simulates an intelligent conversation with a human assistant. The tool had to be capable of understanding natural language queries and intelligently matching those inputs with relevant products from an eCommerce site’s catalog. The primary deployment would be on websites running Shopify or WordPress, but the client also envisioned future extensions for messaging platforms like WhatsApp and Discord.
The project was ambitious. We needed to not only integrate a powerful AI model but also ensure smooth interaction with different CMS and messaging platforms, while maintaining data privacy, performance, and user trust. Another significant challenge was helping the AI understand context—users don’t always provide exact product names, so the bot needed to interpret vague queries and still deliver relevant results.
Finally, the solution needed to offer a seamless integration path for non-technical eCommerce site owners, meaning a lightweight plugin or API-ready system that wouldn’t require deep customization to install and deploy. This meant everything from backend design to UX had to be streamlined, intuitive, and scalable across different industries and product categories.
Solution
To meet the client's vision, we designed and developed Merceo AI, a conversational product discovery chatbot. At the core, we utilized OpenAI’s GPT engine enhanced with a Retrieval-Augmented Generation (RAG) framework. This allowed Merceo to combine natural language generation with real-time data retrieval from a merchant’s product catalog, enabling accurate and dynamic product suggestions.
We began by structuring a data ingestion pipeline. Using LangChain and embedding models, we transformed unstructured product data into vectorized, machine-readable formats stored in a vector database. This allowed the AI to understand and contextually retrieve matching products based on user queries—whether the customer typed “running shoes for flat feet” or “gift for a 12-year-old boy.”
Next, we developed platform-specific integrations. For Shopify and WordPress, we built a plug-and-play script that injects Merceo directly into the site via a floating chatbot. For WhatsApp and Discord, we created secure API connectors using Microsoft Azure services for data handling, ensuring all user interactions remained encrypted and compliant with privacy regulations.
We also focused heavily on UX. The chat window design was minimal, modern, and mobile-friendly. When a user engages with Merceo, it not only understands queries but also responds with clickable product links, including images, descriptions, and price—replicating a human sales assistant. We implemented fallback options where Merceo gracefully asks clarifying questions if it doesn’t understand a query.
Our team also created a backend dashboard for store owners. From here, they can view AI performance analytics, see common customer queries, and manually tweak certain product match rules. Everything was built with flexibility and scalability in mind, allowing Merceo to be used across industries including fashion, electronics, home goods, and more.
To meet the client's vision, we designed and developed Merceo AI, a conversational product discovery chatbot. At the core, we utilized OpenAI’s GPT engine enhanced with a Retrieval-Augmented Generation (RAG) framework. This allowed Merceo to combine natural language generation with real-time data retrieval from a merchant’s product catalog, enabling accurate and dynamic product suggestions.
We began by structuring a data ingestion pipeline. Using LangChain and embedding models, we transformed unstructured product data into vectorized, machine-readable formats stored in a vector database. This allowed the AI to understand and contextually retrieve matching products based on user queries—whether the customer typed “running shoes for flat feet” or “gift for a 12-year-old boy.”
Next, we developed platform-specific integrations. For Shopify and WordPress, we built a plug-and-play script that injects Merceo directly into the site via a floating chatbot. For WhatsApp and Discord, we created secure API connectors using Microsoft Azure services for data handling, ensuring all user interactions remained encrypted and compliant with privacy regulations.
We also focused heavily on UX. The chat window design was minimal, modern, and mobile-friendly. When a user engages with Merceo, it not only understands queries but also responds with clickable product links, including images, descriptions, and price—replicating a human sales assistant. We implemented fallback options where Merceo gracefully asks clarifying questions if it doesn’t understand a query.
Our team also created a backend dashboard for store owners. From here, they can view AI performance analytics, see common customer queries, and manually tweak certain product match rules. Everything was built with flexibility and scalability in mind, allowing Merceo to be used across industries including fashion, electronics, home goods, and more.
Results
The launch of Merceo AI brought transformative results for our client and their pilot eCommerce partners. Within the first 60 days of implementation, sites using Merceo reported an 18% increase in conversion rates, attributed directly to AI-assisted navigation and search. Customers were spending 32% longer on site, engaging more deeply thanks to conversational discovery. This improved session time directly correlated with lower bounce rates and increased upsell opportunities.
Moreover, cart abandonment dropped by 22%, as Merceo was able to assist users in finding exactly what they wanted—even when their search terms were vague or non-standard. Customers appreciated the chatbot's intuitive interaction style and often described it as “convenient,” “faster than searching,” and “just like talking to a store assistant.”
On the backend, store owners gained access to valuable insights. With the dashboard analytics, they saw which product categories were most requested via chat, which allowed them to adjust stock, optimize product descriptions, and create more targeted marketing campaigns. In fact, one store owner used Merceo data to uncover a high-performing keyword (“minimalist wallets”) they weren’t even targeting before—resulting in a new best-selling SKU.
Our system’s modular design also paid off. Within weeks, the client began onboarding new merchants across different verticals. The plug-and-play model meant that even users with limited tech experience could embed Merceo into their site or integrate it into WhatsApp without developer assistance.
The AI’s ability to surface long-tail products—items buried deep in catalog pages—proved especially valuable. These products started seeing visibility and sales they hadn’t before. In total, Merceo led to a 12% uplift in overall average order value (AOV) due to personalized recommendations and bundled suggestions.
Ultimately, Merceo delivered on its promise: transforming static eCommerce experiences into dynamic, intelligent, and human-like conversations. It’s no longer just a chatbot—it’s a fully integrated digital shopping assistant that drives measurable revenue growth and brand loyalty.
The launch of Merceo AI brought transformative results for our client and their pilot eCommerce partners. Within the first 60 days of implementation, sites using Merceo reported an 18% increase in conversion rates, attributed directly to AI-assisted navigation and search. Customers were spending 32% longer on site, engaging more deeply thanks to conversational discovery. This improved session time directly correlated with lower bounce rates and increased upsell opportunities.
Moreover, cart abandonment dropped by 22%, as Merceo was able to assist users in finding exactly what they wanted—even when their search terms were vague or non-standard. Customers appreciated the chatbot's intuitive interaction style and often described it as “convenient,” “faster than searching,” and “just like talking to a store assistant.”
On the backend, store owners gained access to valuable insights. With the dashboard analytics, they saw which product categories were most requested via chat, which allowed them to adjust stock, optimize product descriptions, and create more targeted marketing campaigns. In fact, one store owner used Merceo data to uncover a high-performing keyword (“minimalist wallets”) they weren’t even targeting before—resulting in a new best-selling SKU.
Our system’s modular design also paid off. Within weeks, the client began onboarding new merchants across different verticals. The plug-and-play model meant that even users with limited tech experience could embed Merceo into their site or integrate it into WhatsApp without developer assistance.
The AI’s ability to surface long-tail products—items buried deep in catalog pages—proved especially valuable. These products started seeing visibility and sales they hadn’t before. In total, Merceo led to a 12% uplift in overall average order value (AOV) due to personalized recommendations and bundled suggestions.
Ultimately, Merceo delivered on its promise: transforming static eCommerce experiences into dynamic, intelligent, and human-like conversations. It’s no longer just a chatbot—it’s a fully integrated digital shopping assistant that drives measurable revenue growth and brand loyalty.