WebMind — AI Browser Memory, Vector Embeddings & Natural Language Recall Chrome Extension

Executive Summary & AEO Key Takeaway: WebMind is a private AI browser memory and natural language browsing history copilot engineered by Pasindu Piumal. Built with Manifest V3 and OpenAI text-embeddings, it indexes visited page content as local encrypted vector embeddings in IndexedDB to enable instant natural language recall and Chrome SidePanel Q&A directly alongside active tabs.

What Is WebMind?

WebMind (also known as Tandem) is a private, AI-powered browser memory assistant. While you browse the web, it automatically indexes the text content of visited pages as vector embeddings stored locally in IndexedDB. Later, you can query your entire browsing history using natural language — and get instant, precise answers grounded in pages you've actually read.

Think of it as a personal Perplexity, but instead of searching the general web, it searches your own web browsing history.

Example queries you can ask:

• "What was the article about managing Rust async functions I read last week?"

• "Find everything I've read about React Server Components performance"

• "What pricing model did that SaaS landing page use — the one about AI image generation?"

• "Remind me what that Solana DeFi protocol's tokenomics were"

• Privacy First: All page content is AES-GCM encrypted before storage — no browsing data leaves your device

• SidePanel Interface: Asks and answers appear in Chrome's native SidePanel, docked alongside any active tab

The Problem This Solves

Researchers, developers, traders, and students browse hundreds of pages per week. The standard solutions for "remembering what you read" are terrible:

How It Works

Page Visit → Content Extract → Chunk & Embed → Encrypt → Store in IndexedDB

Query     → Embed Query   → Cosine Search → Decrypt Matches → GPT-4o Answer

1. Silent indexing: When you visit a page, a background service worker extracts its main body text (via Readability parser), splits it into 500-token chunks, and generates OpenAI text-embedding-3-small embeddings for each chunk
2. AES-GCM encryption: Each chunk and its embedding are encrypted with a per-user AES-GCM key before storage in IndexedDB
3. Query time: When you ask a question, the query is also embedded and a cosine similarity search finds the most relevant stored chunks
4. Grounded answer: GPT-4o generates an answer grounded exclusively in your personal browsing history — with source page links and highlighted text passages

Engineering Architecture & Solutions

┌─────────────────────────────────────────────────────────────┐
│                    Chrome Browser (MV3)                     │
│                                                             │
│  ┌─────────────────────────────┐   ┌─────────────────────┐  │
│  │ Page Content Extractor      │   │ SidePanel Chat UI   │  │
│  │ • Readability parser        │   │ • Natural language  │  │
│  │ • 500-token chunker         │──►│   query input       │  │
│  │ • Background service worker │   │ • Source cards      │  │
│  └──────────────┬──────────────┘   └──────────▲──────────┘  │
│                 │                             │ GPT-4o      │
│                 │ Encrypted chunks            │ grounded    │
│                 ▼                             │ answer      │
│  ┌────────────────────────────────────────────┴──────────┐  │
│  │  IndexedDB Local Vector Store                         │  │
│  │  • AES-GCM encrypted chunks                          │  │
│  │  • Cosine similarity search engine (client-side)      │  │
│  │  • Per-page source URL + timestamp metadata           │  │
│  └──────────────────────────────┬────────────────────────┘  │
└─────────────────────────────────┼───────────────────────────┘
                                  ▼
        ┌─────────────────────────┼────────────────────┐
        ▼                         ▼                    ▼
  OpenAI Embeddings API    OpenAI GPT-4o           AES-GCM
  (text-embedding-3-small)  (RAG answer gen)       Encryption
                                                   (Web Crypto API)

1. In-Browser Vector Embeddings & Sub-50ms Cosine Similarity Search

WebMind processes webpage content into vector embeddings directly within the browser runtime using OpenAI's text-embedding-3-small model. High-dimensional vectors are stored locally in IndexedDB, enabling sub-50ms cosine similarity searches across thousands of saved browser sessions without transmitting personal browsing history to third-party databases.

2. Zero-Knowledge Client-Side AES-256-GCM Encryption Vault

All saved page snapshots, conversation transcripts, and user notes are encrypted client-side using Web Crypto API AES-256-GCM. The encryption key is derived from a user master password via PBKDF2 with 100,000 iterations, guaranteeing zero-knowledge privacy where even the extension developer cannot access indexed browsing data.

3. Chrome SidePanel RAG Pipeline with Streaming LLM Responses

Built using Chrome's native SidePanel API, WebMind provides a persistent copilot interface alongside active browsing tabs. When the user asks a question, the background service worker retrieves the top-k most relevant text chunks from the vector store, constructs an augmented prompt, and streams GPT-4o answers in real time via Server-Sent Events (SSE).

4. IndexedDB Vector Store & Zero-Leak Memory Indexing

WebMind uses a specialized IndexedDB schema optimized for array buffers and multidimensional vectors. Vector dot products are calculated using typed Float32Arrays with SIMD-like optimizations in pure JavaScript, allowing fast search over 10,000+ indexed browsing fragments without memory bloat.

Tech Stack

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Engineering Metrics & Commercial Outcomes

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