SHADOW. — Giving AI Product Teams a Persistent Memory
The Problem
Product teams collect valuable information every day — customer feedback, meetings, decisions, competitor observations, and important discussions.
The problem is that this information is usually scattered across different places.
A decision made during a meeting may be forgotten a few weeks later. Customer feedback may exist in a document but never connect with a previous discussion. As the amount of information grows, it becomes harder for teams to remember the context behind their decisions.
This is the problem we wanted to solve with SHADOW.
What is SHADOW?
SHADOW is an AI-powered product memory system designed to help product teams retain, recall, and reflect on their important information.
Instead of treating every AI conversation as a fresh conversation, SHADOW gives the system access to relevant previous context.
The goal is simple:
Make the signal impossible to lose.
How It Works
SHADOW follows a simple three-step memory process:
- Retain
Important information such as customer feedback, meetings, decisions, and competitor observations can be stored as memories.
Each memory can contain metadata, tags, and document information.
- Recall
When a user asks a question, SHADOW can semantically search its stored memories and retrieve the information that is relevant to the question.
This makes it possible to find connections between information that may have been created at different times.
- Reflect
SHADOW uses the retrieved memories to generate a grounded response.
The answer can include supporting evidence and related memories, making it easier for users to understand where the answer comes from.
Key Features
AI-powered product memory
Customer feedback capture
Meeting memory
Decision tracking with rationale
Competitor observation tracking
Semantic memory search
Memory filtering
Evidence-based AI answers
Related memory discovery
Demo data for testing the system
Secure server-side Hindsight API integration
Architecture
The application follows a server-side architecture:
Browser → API Routes → Hindsight Service → Hindsight Cloud
The browser does not communicate directly with Hindsight.
The Hindsight API key is kept on the server and is never exposed to the browser.
The project also validates inputs and maps errors to safe responses.
Technology
The project uses a modern web stack including:
React
TypeScript
TanStack Start
Vite
Zod
Hindsight Cloud
Semantic memory retrieval
AI-powered reflection
Example Use Case
Imagine a product team working on an e-commerce application.
The team records:
A customer complaint
A product meeting
A decision to change checkout
A competitor observation
Later, a team member asks:
"Why did we decide to change the checkout experience?"
Instead of searching through multiple documents manually, SHADOW can recall related memories and provide a grounded answer with supporting evidence.
Why We Built It
AI assistants are becoming better at generating answers, but answers are only as useful as the context available to the system.
We wanted to explore a different idea:
What if an AI system could remember the important context of a product over time?
SHADOW is our attempt to build that persistent memory layer for product teams.
Hackathon Project
SHADOW was built as a hackathon project to demonstrate how persistent AI memory can be used to connect product information and provide more context-aware answers.
The complete source code is available on GitHub:
https://github.com/paswanthreddy6/shadow-memory-keeper
The Website link is:
https://shadow-memory-keeper.lovable.app/
Conclusion
SHADOW is built around a simple idea: important product knowledge should not disappear just because a meeting ended or a document was forgotten.
By combining structured memory capture, semantic recall, and grounded reflection, SHADOW helps product teams keep their important signals connected.
Retain → Recall → Reflect
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That's the idea behind SHADOW..
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