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MBCS Chatbot

Screenshot of webshop with the chat ui

Mercedes-Benz Customer Solutions GmbH

MBCS is a fast-growing, mid-sized company founded in 2000 as Mercedes-Benz Accessories GmbH and now operating as Mercedes-Benz Customer Solutions GmbH - a 100% subsidiary of the Mercedes-Benz Group AG. Based in Stuttgart-Vaihingen and employing around 300 staff, MBCS develops and distributes original accessories and collections for the Mercedes-Benz, Mercedes-AMG, Mercedes-Maybach, and smart brands. Their offerings are known for top quality, functionality, safety, and meticulous attention to detail.

Chatbot Windows

The three chatbot windows

The chatbot I developed for the Mercedes-Benz Online Shop consists of multiple windows. It starts with a welcome window that includes a link to the privacy policy. Optionally, there is a dedicated privacy window (not shown here). The next window features a handbook with an accordion-style menu, listing possible user questions, explanations of control elements, and important usage guidelines. Finally, the chat window allows users to interact with the Mercedes-Benz Assistant.

Technologies

  • python logoPython
  • flask logoFlask
  • html, css & js logoHTML, CSS & JS
  • pinecone logoPinecone
  • firebase logoGoogle Firebase
  • git logoGit
  • render logoRender
  • openai logoOpenAI

Funtionality

The chatbot offers two input options: Users can either select from predefined choices (displayed as black buttons below the welcome message) or enter their queries in a free-text field. It supports both e-commerce interactions and first-level customer support. Simple support inquiries are handled using its local knowledge database, covering topics like terms and conditions, return policies, and data protection. Additionally, the chatbot is connected to two product databases: A local product database for searching specific items and a vector-based product database, which enables more flexible and intelligent searches by finding similar products based on contextual meaning rather than exact keyword matches. This improves the chatbot’s ability to handle vague or imprecise queries effectively.