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Redson Dev · Idea

AIIntermediateAges 18+A weekend

AI-Powered Customer Feedback Kiosk with Offline NLP

Published July 9, 2026

Small businesses, like independent coffee shops or local boutiques, can gather valuable customer feedback without relying on internet connectivity. This project builds a simple, interactive kiosk that uses an embedded system and offline Natural Language Processing (NLP) to collect and summarize customer comments, providing actionable insights for improving service or product offerings. For instance, a coffee shop in Portland, Oregon, could deploy this to quickly understand what patrons think of their new seasonal latte.

What you'll need

  • 1x Raspberry Pi 4 Model B (4GB RAM)
  • 1x 7-inch Raspberry Pi Touch Display
  • 1x MicroSD card (32GB, A2 rated)
  • 1x USB microphone
  • 1x USB power adapter (5V, 3A) for Raspberry Pi
  • 1x Enclosure for Raspberry Pi and display (3D-printed or off-the-shelf)
  • 1x USB Keyboard (for initial setup)

Step-by-step

  1. 01

    Prepare Raspberry Pi OS and Dependencies

    Flash Raspberry Pi OS (64-bit) Lite onto the MicroSD card using Raspberry Pi Imager. Insert the card into the Pi and connect the display, keyboard, and power. Boot the system and complete the initial setup. Install necessary libraries for display and audio input: `sudo apt update && sudo apt install -y python3-pip libatlas-base-dev`.

  2. 02

    Install Offline NLP Library

    Install a lightweight, offline-capable NLP library like `spaCy`. Download a small English language model: `pip3 install spacy && python3 -m spacy download en_core_web_sm`. This model runs locally on the Pi, enabling sentiment analysis without an internet connection.

  3. 03

    Develop Feedback Kiosk Application

    Write a Python script that continuously listens for audio input via the USB microphone. Use the `sounddevice` and `numpy` libraries for audio processing. When a customer speaks, transcribe the audio to text using a local speech-to-text engine (e.g., Vosk, which requires a model download).

  4. 04

    Implement Sentiment Analysis and Display

    Process the transcribed text with `spaCy` to perform sentiment analysis (e.g., identifying positive or negative keywords). Display the input text and a sentiment indicator on the touch display. Store the feedback (text and sentiment) in a local SQLite database for later retrieval and analysis.

  5. 05

    Design User Interface and Enclosure

    Create a simple touch-friendly interface using a Python GUI library like `Tkinter` or `PyQt` to guide users and display feedback status. Design or select an enclosure that securely houses the Raspberry Pi, display, and microphone, making it robust enough for public use in a retail environment.

  6. 06

    Develop Data Export and Summary Feature

    Add a function to the Python application to periodically summarize the collected feedback. This could involve counting positive/negative comments or identifying frequently mentioned keywords. Implement a secure way (e.g., USB drive export or local network share) for the business owner to retrieve this summarized data for analysis.

Tips

  • Use a robust power supply to prevent unexpected shutdowns.
  • Place the kiosk in a quiet area to improve speech recognition accuracy.
  • Consider adding a 'Thank you' message on the screen after feedback is submitted.
#ai-at-the-edge#offline-nlp#raspberry-pi#customer-feedback#small-business
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