SHADOW. — Giving AI Product Teams a Persistent Memory
SHADOW. — Giving AI Product Teams a Persistent Memory The Problem
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Product teams collect valuable information every day
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
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.
SHADOW is an AI-powered product memory system designed
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
Instead of treating every AI conversation as a fresh conversation, SHADOW gives the system access to relevant previous context. Make the signal impossible to lose. SHADOW follows a simple three-step memory process:
Important information such as customer feedback, meetings, decisions
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.
When a user asks a question, SHADOW can
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. SHADOW uses the retrieved memories to generate a grounded response.
The answer can include supporting evidence and related
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
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. The project uses a modern web stack including:
React TypeScript TanStack Start Vite Zod Hindsight Cloud
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. 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?"