I Built FeedbackLens to Remember What Feedback Changed

ravithreni gujjula — Tue, 29 Sep 2026 15:40:18 +0000

Customer feedback is everywhere. It arrives through reviews, support conversations, surveys, emails, app ratings, and direct messages. The difficult part is not simply reading or classifying that feedback. The real challenge is remembering what customers complained about before, recognizing whether the same problem is happening again, and understanding whether a product change actually improved the situation.

That is the problem I wanted to address with FeedbackLens, an AI-powered customer feedback intelligence platform built around long-term Hindsight memory.

GitHub Repository: https://github.com/deepikamekala01/FeedbackLens

Instead of treating every feedback message as an isolated piece of text, FeedbackLens looks at feedback as a timeline. It asks a more useful question:

“Have we seen this problem before, and did anything we changed actually fix it?”

The Problem with One-Time Feedback Analysis

Traditional AI feedback analysis can be useful. Give an AI system a customer message and it can determine whether the sentiment is positive or negative, classify the issue, estimate its severity, and identify the affected product area.

For example, a customer might write:

“Checkout is taking too long.”

An AI system can immediately classify this as a negative complaint related to checkout. But that answer alone leaves out important context.

What if several customers complained about the same issue over the previous few weeks?

What if the company released a checkout optimization after those complaints?

What if customers initially reported improvement, but the complaints started appearing again a few days later?

A single analysis cannot answer these questions unless the system can remember and connect previous events.

That is where FeedbackLens uses persistent memory.

How FeedbackLens Works

The basic workflow is:

Customer Feedback → AI Analysis → Hindsight Memory → Historical Context → Feedback Intelligence → Product Action

When new feedback is submitted, FeedbackLens analyzes it using an AI reasoning engine. The current system identifies four important attributes:

Sentiment
Category
Severity
Product Area

For example, the feedback “checkout taking time” can be identified as negative sentiment, a bug-related complaint, medium severity, and related to the checkout product area.

But the analysis does not stop there.

FeedbackLens uses a Hindsight memory bank to recall relevant historical customer feedback. The system can then connect the new complaint with previous complaints about the same product area.

This transforms the question from:

“What does this customer say?”

into:

“What does this customer say in the context of everything we already know?”

A Real Example: The Checkout Problem

The strongest example in FeedbackLens is a recurring checkout issue.

The memory timeline contains customer complaints such as:

Sep 01: Checkout takes too long to load
Sep 03: Payment page freezes
Sep 05: Mobile checkout is slower than desktop
Sep 08: Payment failed twice
Sep 10: Checkout took more than 20 seconds
Sep 13: Payment screen crashes
Sep 16: Checkout is very slow on Android
Sep 18: Payment delayed / transaction failed
Sep 21: Mobile checkout keeps loading
Sep 24: Mobile checkout needs to be faster

Looking at these individually makes them appear to be separate complaints.

Looking at them together reveals a pattern.

The system identifies a recurring theme around slow checkout performance, particularly on mobile devices, along with payment reliability issues.

Then something important happens in the timeline.

September 25: Checkout Optimization v2.1

A product release is recorded:

Checkout Optimization v2.1

The goal of the release is to improve mobile checkout speed and payment reliability.

Now the interesting part begins.

After the release, FeedbackLens sees:

Sep 26: Checkout feels much faster
Sep 27: Payment went through quickly
Sep 28: Checkout still slow; payment failed
Sep 29: Payment failed again on mobile

A conventional system might simply report the positive feedback after the release and conclude that the update helped.

FeedbackLens has more context.

Because it remembers what happened before and after the release, it can recognize that the improvement was partial rather than complete.

The release appears to have improved checkout performance for some interactions, but recurring complaints—particularly around mobile checkout and payment failures—continued to appear.

This is the type of insight that becomes possible when feedback is connected across time.

From Feedback to Product Intelligence

FeedbackLens does more than display historical messages. It synthesizes them into information that can help a product team understand what is happening.

The current system highlights:

Recurring Theme

Slow checkout performance, especially on mobile devices.

Trend

The issue remains persistent over time, and the previous fix only partially mitigated the slowdown.

Key Insight

Checkout latency remains a high-impact problem, with mobile users experiencing particularly poor performance. The continued payment failures suggest that the underlying problem has not been completely resolved.

Recommended Action

The system recommends a deeper investigation into mobile checkout latency, including profiling client-side and server-side performance, investigating API latency, and monitoring checkout performance by device.

This creates a path from raw customer comments to an actionable product decision.

Why Hindsight Memory Matters

The important part of FeedbackLens is not simply that it stores old feedback.

Memory becomes valuable when it changes how the system interprets new information.

Hindsight allows FeedbackLens to retrieve relevant historical context when analyzing new feedback. Instead of starting from zero every time, the system can connect the current complaint with previous experiences stored in its memory bank.

This also means that memory is different from retraining the AI model.

The underlying model does not need to be retrained every time a customer submits feedback. Instead, relevant experiences are retained and recalled when they can provide useful context.

This approach allows the system to build a continuous context around customer experiences without treating every new interaction as an isolated event.

The Feedback Timeline Becomes the Story

One of the most important design decisions in FeedbackLens is making the history visible.

The Memory Timeline shows customer feedback chronologically and places the product release directly inside that timeline.

This makes it possible to visually understand:

Problem → Repeated Complaints → Product Change → Initial Improvement → Recurring Problems

That sequence is much more meaningful than a collection of isolated sentiment scores.

It also makes product changes easier to evaluate. Instead of asking only, “Was the release successful?”, a product team can ask:

Did customer complaints decrease?
Did the same issue return?
Which problems remain unresolved?
Did customer sentiment change after the release?
Are certain devices or platforms affected more than others?

These questions require historical context.

What I Learned Building FeedbackLens

The project reinforced several important lessons about AI systems with memory.

First, memory should change the answer.

Simply storing information is not enough. Retrieved memories should provide context that improves the system's interpretation.

Second, events become more useful when connected.

A single complaint may not reveal much. A sequence of related complaints around the same product area can reveal a persistent problem.

Third, product changes need historical context.

A release cannot be evaluated properly by looking only at feedback immediately after deployment. Earlier complaints and later complaints both matter.

Fourth, memory should be visible.

Showing the recalled context and timeline makes it easier to understand why the AI reached its conclusion.

Finally, the most valuable question is often about change.

Not just:

“What are customers complaining about?”

But:

“What changed after we acted on their complaints?”

Looking Ahead

FeedbackLens can be extended to connect feedback from multiple sources and maintain a longer history of customer experiences, product decisions, releases, and unresolved issues.

Over time, this could help teams identify emerging themes earlier, monitor how customer sentiment changes around product releases, and understand which customer problems continue despite previous attempts to solve them.

The goal is simple: turn customer feedback from a collection of messages into a continuously evolving source of product intelligence.

Feedback is valuable when it is heard.

It becomes much more valuable when it is remembered, connected, and understood in context.

FeedbackLens brings those pieces together: remember the feedback, connect the history, and learn what changed.

DEV Community: ravithreni gujjula

I Built FeedbackLens to Remember What Feedback Changed