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README.pt-BR.md
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architecture.md
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privacy-decisions.md
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recruiter-notes.md
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README.md

ApplyFlow Case Study

Portuguese version: README.pt-BR.md

ApplyFlow is a local-first job application workflow assistant focused on candidate productivity, application tracking, reusable answers, job intelligence and privacy-first workflow design.

This case study documents the product vision, architecture and engineering decisions behind ApplyFlow.

Case Study Documents

Document Purpose
Architecture Extension/dashboard boundaries, local-first model, shared domain logic and optional AI layer
Privacy Decisions Local-first data, optional AI, no blind submission and data portability
Recruiter Notes What recruiters and interviewers should evaluate in this case

Product Context

Applying to jobs is repetitive, hard to track and often poorly organized. Candidates commonly use spreadsheets, browser history, notes and manual reminders to manage applications.

ApplyFlow was designed to bring structure, visibility and safer workflow automation to the job application process.

Problem

Candidates face several operational problems:

  • Losing track of applications
  • Repeating the same profile information
  • Not knowing which jobs are worth prioritizing
  • Having no clear funnel metrics
  • Storing career data across disconnected tools
  • Risking low-quality applications through blind automation

Solution

ApplyFlow provides a local-first workflow assistant with application history, reusable answers, job analysis, metrics and optional AI coaching.

The product prioritizes quality, control and privacy over mass automation.

Core Principles

  • Local-first data handling
  • Privacy-first product design
  • Human-in-the-loop workflow
  • No blind submission
  • Portable JSON export/import
  • Optional AI layer
  • User-controlled career data

Architecture Overview

Browser Extension Layer
  ├── Page interaction concept
  ├── Local storage
  ├── Assisted field suggestions
  └── User review flow

Shared TypeScript Packages
  ├── Domain contracts
  ├── Job analysis helpers
  ├── Candidate data structures
  └── Import/export schemas

Next.js Dashboard
  ├── Application funnel
  ├── Metrics
  ├── Job tracking
  ├── Answer bank
  └── Optional AI coaching

Technical Stack

Frontend

  • Next.js
  • React
  • TypeScript
  • Tailwind CSS

Extension

  • Chrome Extension MV3 concept
  • Browser local storage
  • Content integration patterns

Core

  • TypeScript packages
  • Local-first contracts
  • JSON import/export
  • Heuristic analysis

AI

  • Optional OpenAI integration
  • User-provided API key
  • AI-assisted coaching and analysis

Key Features

  • Application tracking
  • Job description analysis
  • Reusable answer bank
  • Funnel metrics
  • JSON export/import
  • Optional AI coaching
  • Local-first data model

Product Differentiation

ApplyFlow is not a mass-apply bot. It is a workflow assistant designed to improve candidate organization, quality and visibility while keeping the user in control.

Business Value

ApplyFlow demonstrates product thinking around privacy, workflow automation, browser-based productivity and AI-assisted career tools.

Roadmap

  • Add richer job matching
  • Add interview preparation flows
  • Add public demo mode
  • Add encrypted backup option
  • Add screenshots and demo video
  • Add integration with Career Suite

Links