Imagine a product team receiving thousands of feedback messages every month.
One user complains about a payment failure today.
Another reported the same problem two months ago.
A third mentions it again after a new release.
A traditional feedback dashboard may show these as three separate complaints.
But what if an AI system could remember them and connect the dots?
That is the idea behind User Feedback Synthesizer — an AI-powered platform that analyzes user feedback and uses long-term memory to connect current feedback with historical context.
We built this project to explore a simple question:
Can an AI system understand user feedback better when it remembers what users said before?
The Problem
User feedback is everywhere — reviews, ratings, surveys, support conversations, feature requests, and feedback forms.
The difficult part isn't collecting the feedback.
The difficult part is understanding what it means over time.
When feedback grows into thousands of records, manually reading and comparing them becomes difficult. Traditional dashboards can show sentiment percentages, ratings, keywords, and trends, but they often treat individual feedback records independently.
Consider these three examples:
January
"Payment sometimes fails during checkout."
February
"The payment page is slow and occasionally fails."
March
"Payment failed again after the latest update."
A basic feedback system may analyze these as three separate complaints.
But from a product perspective, there may be a much more important story:
There could be a recurring payment-related problem.
This is the gap we wanted to address.
What We Built
We built User Feedback Synthesizer, a feedback intelligence platform that combines:
- AI-powered feedback analysis
- PostgreSQL for structured data
- FastAPI for backend services
- React/Vite for the frontend
- Hindsight for long-term memory
The key idea is:
Don't just analyze what users said. Remember what they said before.
The system analyzes feedback, extracts useful information, stores structured data, retains important context, and recalls relevant historical information when new feedback is analyzed.
This allows the AI to work with both current feedback and historical context.
How the System Works
At a high level, the workflow looks like this:
User Feedback
↓
Data Processing
↓
AI Analysis
↓
PostgreSQL
↓
Hindsight Memory
↓
RETAIN / RECALL
↓
Historical Context
↓
AI Agent
↓
Contextual Insight
↓
Dashboard
System Architecture
Our architecture connects the feedback, application, data, AI, and memory layers into one workflow.
The architecture connects feedback sources, the application layer, structured data storage, AI analysis, and Hindsight long-term memory to generate context-aware insights.
Building the Dashboard
We built the frontend using React and Vite.
The dashboard provides a way to explore the feedback intelligence generated by the system.
It brings together information such as:
- Feedback analytics
- Sentiment distribution
- Major themes
- Important issues
- Trends
- Recent concerns
- AI-generated insights
Users can also explore individual feedback records and interact with the AI using natural-language questions.
Instead of manually searching through large amounts of feedback, users can ask questions and let the system synthesize relevant information.
The dashboard brings feedback analytics, trends, themes, and AI-generated insights together in a single interface.
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