InsightLoop

Inspiration

Small and medium-sized businesses (MSMEs) receive customer feedback through Google Reviews, WhatsApp, social media, and voice calls. However, understanding feedback in multiple languages and identifying customer concerns manually can be difficult and time-consuming. This inspired us to develop FeedbackForge AI, an intelligent platform that transforms customer feedback into useful business insights.

What It Does

FeedbackForge AI is designed to collect and analyze multilingual text and voice feedback. It converts voice input into text using speech-to-text technology and applies Natural Language Processing (NLP) and Large Language Models (LLMs) to understand customer opinions. The platform aims to perform sentiment analysis, identify important feedback topics, detect potentially fake reviews, and analyze customer trends. A dashboard presents the results to help MSMEs make informed business decisions.

How We Built It

We planned the solution as a modular AI pipeline. Customer feedback is collected from text and voice sources, followed by speech-to-text conversion for audio, language identification, and text preprocessing. The processed feedback is analyzed using NLP and LLM-based techniques. Sentiment analysis identifies positive, negative, and neutral opinions, while additional modules are designed for suspicious review detection and trend forecasting. A dashboard provides a user-friendly way to view the results. The proposed technology stack includes Python, Whisper, transformer-based NLP models, machine learning tools, a database, and Streamlit.

Challenges We Faced

One major challenge is handling multilingual feedback, especially Tamil and English, including mixed-language sentences. Voice recordings may contain background noise, unclear pronunciation, and regional accents. Another challenge is distinguishing suspicious reviews from genuine customer opinions. Integrating different AI modules into a single workflow and ensuring that the results are reliable are also important challenges.

What We Learned

Through this project, we explored how AI, NLP, LLMs, speech-to-text technology, and machine learning can address real-world business problems. We learned the importance of data preprocessing, multilingual support, model evaluation, and designing dashboards that present information clearly. We also understood that AI-generated insights should be validated before being used for business decisions.

What's Next

We plan to improve Tamil and other Indian-language support, evaluate sentiment and fake-review detection performance, and enhance trend forecasting. Future development may include real-time feedback integration, improved voice recognition, and additional dashboard features. Our goal is to make customer feedback analysis more accessible and useful for MSMEs, helping them understand customers and improve their services.

Built With

• artificalintelligence
• generativeai
• machine-learning
• python
• sentimentanalysis
• speechtotext