A sophisticated AI-driven product recommendation chatbot Airtable connected

Use case

How developing a sophisticated AI-driven product recommendation chatbot could help a a high-end organic skincare retailer

This use case explores how after a comprehensive discovery phase, which underlined the unique demands and preferences of their discerning clientele, an analysis led to the development on a product recommendation table. Faced with the challenge that traditional product recommendation methods were insufficient for the client’s intricate requirements, the project pivoted towards leveraging AI technology. This approach was instrumental in surmounting the challenges, thereby highlighting AI’s significant potential in crafting personalized customer experiences.


The organic skin care company faced several challenges:

Diverse Customer Profiles: Organic skincare customers often have varied skin types, concerns, and preferences. Developing a chatbot that accurately understands and responds to such diverse needs can be challenging.

Ingredient Transparency: Customers interested in organic skincare are typically very knowledgeable and concerned about ingredients. The chatbot must be equipped to provide detailed, accurate information about the ingredients in each product.

Personalization: Personalized product recommendations are crucial in skincare. The chatbot needs to make recommendations that are not only relevant to the customer’s skin type and concerns but also align with their preferences and values, like vegan or cruelty-free products.

Educational Content: Many customers may not be fully aware of the benefits or uses of certain organic skincare products. The chatbot should educate customers and guide them in their skincare journey.

Data Privacy and Security: Handling customer data, especially sensitive information about skin concerns or personal preferences, requires strict adherence to data privacy and security protocols.

Integration with Existing Systems: The chatbot must seamlessly integrate with the company’s existing inventory, customer relationship management, and e-commerce platforms to provide accurate and up-to-date information.

Handling Complex Queries: Skincare inquiries can often be complex. The AI chatbot must be sophisticated enough to understand and respond accurately or escalate the query to a human when necessary.

Cultural and Linguistic Barriers: As skincare is a global industry, the chatbot might need to cater to customers from different cultural backgrounds and languages, which can be a significant challenge in terms of localization and communication.

Constant Updating and Learning: Skincare trends and customer preferences evolve rapidly. The AI system must continuously learn and update itself to stay relevant.

User Experience: Ensuring the chatbot is user-friendly, engaging, and provides a positive experience is crucial for customer satisfaction and retention.

Project requirements

  • Building an AI-powered chatbot capable of handling a specialized conversation flow.
  • Integration with an Airtable database for email verification and VIP list management.
  • Customized responses based on customer inputs about product preferences and pain points taking into account a knowledge base

The solution

  • Development of the chatbot with detailed functionalities:
    • Email verification against an existing database and adding new entries.
    • Personalized greetings and engagement based on customer’s purchase history and preferences.
    • Product recommendations tailored to customer’s specific needs, with options for cross-selling and up-selling.
    • Dynamic generation of product links based on customer selections, including size and color.
  • Integration with Airtable for real-time data tracking and updating customer preferences.
  • Use of Airtable’s API for data retrieval and updates.


The developed solution demonstrated substantial potential:

  • Advice on skin problems offered to customers. The solution’s flexibility in handling various customer queries within predefined parameters.
  • Insights into customer preferences and behavior, aiding in refining marketing and sales strategies.
  • Growing customer database with detailed information on customers preferences. 

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