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How an AI chatbot can transform ecommerce customer support

Overshoulder shot of a woman using an ecommerce platform conducted a successful proof of concept (PoC) for an AI chatbot that can be used in various industries, like banking and healthcare, aiming to enhance communication, improve response times, and efficiently handle customer queries.

Line of business



In e-commerce, companies acknowledge the need to enhance communication between the company and its clients to improve response speed.

Solution implemented a proof of concept (PoC) for an AI chatbot on its website. The primary objective was to assess how the chatbot could enhance communication, improve response times, and effectively handle customer queries and concerns.

Which technologies have we applied:

  • Python3,
  • LangChain,
  • Figma,
  • React,
  • Firebase,
  • FastAPI,
  • Kubernetes.
  • OpenAI API (LLM, ChatGPT)
chatbot technologies used

The role of chatbots in company challenges.

The companies face challenges in effectively addressing customer queries, concerns, and feedback in a timely manner. Many users ask the same questions repeatedly, resulting in a need for a solution to handle such repetitive inquiries. 

Implementing a chatbot was seen as a way to address these issues effectively. Currently, numerous employees are dedicated to answering live chat questions. Automating this process through a chatbot would reduce the need for human intervention and free up employees for more strategic tasks.

Moreover, training employees to handle the extensive information required for live chat responses is time-consuming. On the other hand, training a chatbot is much faster and more efficient, allowing for quicker implementation.

Due to the implemented chatbot, companies can work towards achieving complete automation of their customer support process. By utilizing a chatbot, they can streamline communication, reduce reliance on human resources, and provide rapid and accurate responses to customer inquiries.

AI chatbot

The people and tech behind our project.  

Our team was composed of a diverse set of experts, including two UX/UI specialists, one front-end developer, one DevOps specialist, and four specialists in Natural Language Processing and backend development.

In the project, we employed a range of technologies for different aspects:

For design, we used Figma. React was utilized for frontend development. The backend consisted of Firebase as well as Python 3 with LangChain. FastAPI was implemented to handle requests efficiently. For service management, we employed Kubernetes. 

Enhancing our chatbot’s performance.

We have started with research on the feasibility of using various LLMs as a backbone of our chatbot, and ultimately chose OpenAI to power our chatbot. 

We carefully designed the chatbot, ensuring it aligned perfectly with our brandbook. Once the design was complete, we developed the backend. In just two weeks, we performed the integration with the frontend successfully.

Having a successful chatbot heavily depended on the data it received, as wrong context could lead to poor performance. So, we carefully curated the ideal dataset for fine-tuning to ensure its optimal functionality.

After successful deployment, we collected internal feedback to evaluate the chatbot’s performance in handling both simple and complex questions, and also run a set of automated tests to evaluate its performance.

chatbot DACdigital

The impact of our chatbot’s PoC on the entertainment industry

  • We successfully created the entire Proof of Concept (PoC), demonstrating that the chatbot is capable of understanding user queries and providing relevant answers based on the fine-tuned model. 
  • The project was completed within the allocated time frame and budget, which spanned two months and encompassed design, backend and frontend development, as well as machine learning implementation. 
  •’s team considers additional features for future implementation, such as sentiment analysis of the inquires, support for multilingual operations, further enhancement of the contextual understanding and integration with external systems.
chatbot healthcare banking

Cross-industry efficiency: the impact of chatbots in diverse sectors.

Chatbots are a great solution that go beyond e-commerce, demonstrating their efficacy in a variety of industries like banking and healthcare. Users frequently grow impatient while waiting for simple question answers, and they eagerly look forward to prompt responses.

Chatbots in the banking industry provide a helpful solution to this frequent problem. Customers can get prompt answers to their questions by utilizing chatbot technology, which will reduce their dissatisfaction and improve their overall experience.

By giving doctors helpful support, chatbots can also have a big impact in the field of medicine. These intelligent systems can talk to patients, learn about their symptoms, and then transmit the information instantly to doctors.

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