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*** Begin Reindexed PRD*

Kaizen — Product Requirements Document (PRD)

Version: 0.1 — Hackathon MVP Date: 2025-10-15

Table of contents

  1. Objective & Scope
  2. Problem Statement
  3. Vision & Value Proposition
  4. Target Users & Personas
  5. System Overview
  6. MVP Features (user stories + acceptance criteria)
  7. Architecture & Key Components
  8. Tech Stack & Non-functional Requirements
  9. Roadmap & Milestones
  10. Success Metrics & Risks
  11. Resources

1. Objective & Scope

Objective: build a privacy-first Chrome extension that turns Chrome into a locally-run AI co-pilot for browsing (Kaizen). The scope for the hackathon MVP (20 days) focuses on local summarization, basic tab automation, voice/text commands, and a minimal knowledge graph.

In scope for MVP:

  • Voice and text command interface (popup)
  • Tab grouping and management
  • Local summarization for active tab / grouped tabs
  • Lightweight RAG knowledge graph visualization (side-panel)
  • Doomscroll detection with behavioral nudge
  • Privacy & consent summarizer

Out of scope for MVP:

  • Cloud sync / account integration
  • Full-scale offline LLM training
  • Advanced multi-device sync

2. Problem Statement

People hesitate to use AI browser features due to privacy, lack of control, and latency from cloud inference. Kaizen solves this by providing an on-device, privacy-focused assistant using Gemini Nano and Chrome-built-in AI APIs.

3. Vision & Value Proposition

Kaizen transforms Chrome into a context-aware assistant that helps research, automates repetitive browsing tasks, and protects users from unsafe content — all without sending their browsing data to remote servers.

Key value props:

  • Privacy-first local AI
  • Context-aware automation (tabs, groups, navigation)
  • Lightweight knowledge graph to surface relationships

4. Target Users & Personas

Primary users:

  • Researchers and knowledge workers who open many tabs and want to synthesize findings quickly
  • Power users who automate workflows (tab management, autofill)

Secondary users:

  • Privacy-conscious users who want AI assistance without cloud data sharing

5. System Overview

High-level flow:

  • User issues a command (voice or text) in popup → AI Command Layer parses intent → Background/AI service performs actions (tabs, summarize, build graph) → UI updates in popup/side-panel.

Core runtime boundaries:

  • UI (popup/side-panel) — React + Vite dev server for development
  • Background service worker — message routing and tabs API
  • Content scripts — page-level extraction and behavioral monitoring
  • Local AI core — Gemini Nano via Chrome Built-in AI APIs (production)

6. MVP Features (User stories + Acceptance Criteria)

6.1 Voice & Text Command Interface

  • Story: As a user, I want to tell Kaizen to "summarize this tab" so I get a short summary without leaving the page.
  • Acceptance: Popup accepts text/voice input, returns a 2–4 sentence summary for the active tab.

6.2 Tab Grouping & Management

  • Story: As a researcher, I want Kaizen to group tabs related to a topic.
  • Acceptance: Kaizen can create a tab group and move selected tabs into it via a command.

6.3 Local Summarization & RAG

  • Story: As a user, I want summaries of a tab group and a lightweight knowledge graph of extracted concepts.
  • Acceptance: Kaizen produces a group summary and a small graph (nodes/edges) for the session.

6.4 Doomscroll Detection & Nudge

  • Story: As a user, I want Kaizen to detect extended scrolling and offer a summary or a break.
  • Acceptance: After N minutes of continuous scrolling on the same domain, Kaizen displays a nudge in the popup.

6.5 Privacy & Consent Summarizer

  • Story: As a user, I want Kaizen to detect consent dialogs or policy text and provide a short risk summary.
  • Acceptance: On page load, Kaizen flags major consent text and provides a 1–2 line summary.

7. Architecture & Key Components

7.1 Frontend

  • Popup (voice/text input, actions)
  • Side-panel (knowledge graph visualization)

7.2 Background (API Gateway)

  • Routes messages, manages state, executes tab actions

7.3 Content Scripts

  • Extract page text, detect doomscrolling and consent banners

7.4 Local AI (Gemini Nano)

  • Responsible for summarization, rewriting, and graph embedding generation

7.5 Storage

  • Chrome storage / IndexedDB for session graphs and local preferences

8. Tech Stack & Non-functional Requirements

Tech choices:

  • Frontend: React + Vite
  • Graph: Cytoscape.js or D3
  • Storage: IndexedDB / chrome.storage.local
  • Dev: pnpm workspaces, turbo (optional)

Non-functional:

  • Privacy: no network calls for content by default
  • Performance: local inference should not block UI; use web workers where appropriate
  • Compatibility: Chrome stable supporting Chrome Built-in AI APIs (note feature flags may be required)

9. Roadmap & Milestones (Hackathon)

Day 1–3: scaffold, popup UI, background routing Day 4–8: local summarization + content extraction Day 9–13: tab grouping + side-panel graph UI Day 14–18: doomscroll detection + privacy summarizer Day 19–20: polish, testing, demo prep

10. Success Metrics & Risks

Success Metrics (for MVP):

  • Completion of 6 core features above
  • Demoable local summarization latency < 2s (for short pages)
  • User flow time-to-summarize < 10s end-to-end

Top risks:

  • Gemini Nano availability and compatibility across Chrome versions
  • Local performance and memory usage
  • Permission and manifest restrictions for side panel APIs

Mitigations:

  • Fall back to lighter summarization methods; use web workers; document required Chrome flags

11. Resources

End reindexed PRD