MarketPulse

• 

  Logo of MARKETPULSE

• 

  HOW IT APPEARS FOR ME (ADMIN)

• 

  THE RESTAURANT

• 

  HOW AI WORKS

• 

  HOW AI WORKS

• 

  HOW AI WORKS

Inspiration

Local businesses receive valuable feedback every day through online reviews, but that information often stays buried inside individual comments. I wanted to explore a simple question: what if customer reviews could become a practical tool for helping local businesses improve? MarketPulse started from our experience building a review-tracking tool and realizing that a rating alone does not tell the whole story. The real value is in understanding what customers are actually saying.

What it does

MarketPulse helps local businesses turn customer feedback into useful business information. The current MVP collects business information and recent Google Maps reviews, stores individual reviews, and keeps the data updated. The next layer is AI: instead of making a business owner read through reviews one by one, MarketPulse F is designed to identify what the business is doing well, what customers think could be improved, and what action could be taken next. The goal is simple: Real reviews → useful insights → better decisions.

How I built it

MarketPulse evolved from an existing review-tracking web application. I built the application using React and TypeScript, with Supabase and PostgreSQL for the backend and database, Vercel for deployment, and Apify to collect Google Maps business and review data. I extended the existing system to store individual reviews instead of only storing aggregated ratings and review counts. This gives MarketPulse a structured source of real customer feedback that can be analyzed by an AI layer.

Challenges we ran into

One of our biggest challenges was working with real-world review data while keeping the system simple and reliable. I also had to think carefully about API costs, synchronization frequency, duplicate reviews, and how to store only the information actually needed. Another challenge was deciding what the AI should actually provide. We did not want a complicated analytics dashboard that gives businesses more data without helping them make decisions. Instead, we focused on three simple questions:

• What are we doing well?
• What should we improve?
• What should we do next?

Accomplishments that we're proud of

I am proud that MarketPulse grew from a working review-tracking application into a foundation for an AI-powered product without rebuilding the entire system from scratch. We successfully integrated real Google Maps review data into the application and created a structured review database. In our first controlled test, MarketPulse successfully collected and stored all 10 available reviews from a real local business. This showed me that the technical foundation for the product works with real-world data.

What we learned

I learned that a star rating is only the beginning. A 5-star rating tells us that a customer was satisfied, but the review text can tell us why. That difference is what makes customer feedback valuable as a source of business intelligence. I also learned that building for small businesses does not necessarily require complicated technology. Sometimes the most useful product is the one that takes complex information and turns it into one clear decision.

What's next for MarketPulse

The next step is completing the AI layer. I want MarketPulse to analyze recent customer reviews and give business owners a short, practical summary of what customers are saying. From there, I want to explore how the same approach could help businesses identify trends over time, understand recurring problems, discover opportunities, and eventually help entire local communities become more responsive to the people who use them.

MarketPulse is built around one idea: businesses become stronger when they listen to their customers.

Built With

• apify
• claude
• github
• google
• maps
• postgresql
• react
• supabase
• typescript
• vercel
• vite