ReviewIQ

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Inspiration

Small businesses rely heavily on their reviews and ratings, but many business owners don’t know what to do with them, where they are falling short of their competitors, or how to take their business to the next level. Looking through all of those reviews the hundreds or thousands of reviews that businesses have takes a huge amount of valuable time and effort. This is why we were inspired to make ReviewIQ, after realizing how much businesses struggle with turning feedback into useful decisions.

We wanted to create a tool that turns customer feedback into clear insights that business owners could easily act on.

What it does

ReviewIQ is an AI-powered review analytics platform that helps businesses understand and improve their online reputation.

Users can search for a business, and ReviewIQ will collect review data, analyze customer sentiment, look for common complaints and positive trends, and compare the business against nearby competitors.

Key features include:

Real-time analysis of actual reviews and data
AI-powered sentiment analysis
Identification of common customer complaints and strengths
Competitor benchmarking using nearby businesses
Tracks review trends over time.
Insights to help businesses improve customer experience

Instead of just showing businesses their reviews, ReviewIQ gives actionable insights on them and what they should do next.

How we built it

ReviewIQ was built using a full-stack structure.

The backend was made with Python and FastAPI, handling API requests, review processing, competitor discovery, and data analysis. We also integrated SerpAPI and GroqAPI to gather review data and analyze it from Google Maps.

The frontend was built with React and Vite, creating an interactive dashboard where business owners can view reviews, insights, and competitor comparisons.

ReviewIQ uses AI analysis to track patterns from raw data and reviews and turn them into meaningful recommendations for businesses.

Challenges we ran into

One of the biggest challenges that we faced was the process of integrating the APIs and using them to extract accurate and useful data from Google.

Some other challenges we ran into include: -Finding a reliable way to get data online from reviews (APIs) -Managing API limits while using more searches for testing -Using SerpAPI to get the data and GroqAPI to generate AI feedback -Designing the system by working with Claude and using VSCode.

Accomplishments that we're proud of

We are proud that we built a working application that can:

Search real businesses
Retrieve real customer reviews
Analyze customer feedback
Discover competitors automatically
Provide businesses with useful reputation insights

We also built a system that optimizes API usage and handles failures properly, allowing the application to continue working even when external services fail by using backup demo data as a fallback.

What we learned

Throughout this project, we learned how to build and connect a complete software application from frontend to backend.

We gained experience with:

API integration
Backend development
Frontend development
Understanding how VSCode works and how to run a local host
Data processing and AI-powered analysis
Designing software around real user problems

Most importantly, we learned that building a useful product is not only about writing code, but also about understanding a problem and creating something that provides real value.