ApplyFlow is a local-first job application workflow assistant created by DevFlow Labs.
This repository is a public technical case study. It documents product vision, architecture, technical decisions and roadmap without exposing private implementation details.
ApplyFlow combines a browser extension concept, a Next.js dashboard and reusable TypeScript packages to support a safer and more organized job application workflow.
The product focuses on assisted execution, review, tracking and candidate productivity. It is designed to help users organize applications, prepare reusable answers, evaluate opportunities and maintain a local audit trail while keeping sensitive career data under user control.
Job applications create repeated operational work:
- Candidates lose track of where they applied
- Forms often require repeated profile information
- Job descriptions are hard to compare consistently
- Metrics are usually spread across notes, spreadsheets and browser history
- Sensitive career data should be handled carefully
- Candidates need productivity without losing quality or control
ApplyFlow addresses this through a privacy-first and human-in-the-loop approach.
ApplyFlow provides a structured workflow for job applications:
- Application tracking
- Candidate answer bank
- Job description analysis
- Funnel metrics and status tracking
- Local data storage
- JSON export and import
- Optional AI coaching using a user-controlled API key
- Review-first workflow before any final user action
- Local-first: data stays in the user-controlled workflow
- Privacy-first: no backend dependency in the critical MVP path
- Human-in-the-loop: the user remains responsible for final actions
- Auditability: application state should be traceable
- Portability: users can export and import their workflow data
- Quality over volume: the product prioritizes better applications, not mass submission
Browser Extension Layer
└── Content integration concept
└── Background/service worker concept
└── Local application storage
└── Assisted review workflow
Shared TypeScript Packages
└── Core domain contracts
└── Job analysis utilities
└── Candidate data structures
└── Import/export schemas
Next.js Dashboard
└── Application funnel
└── Metrics and insights
└── Job tracking
└── Import/export workflow
└── Optional AI coaching
Storage Strategy
└── Browser local storage
└── Dashboard local storage
└── Portable JSON handoff
└── No backend required for the MVP flow
- Next.js
- React
- TypeScript
- Tailwind CSS
- Component-based UI
- Browser extension architecture
- Local storage
- Content integration patterns
- User-reviewed assistance flow
- TypeScript packages
- Local-first data contracts
- Heuristic job analysis
- JSON import/export
- Optional OpenAI integration
- User-provided API key model
- AI coaching as an add-on, not a hard dependency
Each application can be tracked with status, source, company, role and notes.
The candidate can maintain reusable answers for frequent application questions.
The system can extract useful signals from job descriptions and help the user compare opportunities more consistently.
The dashboard can show application volume, status distribution, funnel progress and productivity indicators.
AI can assist with job fit analysis, answer improvement and interview preparation when explicitly enabled by the user.
The workflow supports export and import through structured JSON.
ApplyFlow is not positioned as a mass-apply tool. Its value is in safer execution, better organization and higher-quality candidate workflows.
| Area | ApplyFlow Approach |
|---|---|
| Data | Local-first |
| AI | Optional and explicit |
| Ownership | User-controlled export/import |
| UX | Review-first workflow |
| Product value | Organization, quality and visibility |
Career data can be sensitive. A local-first model allows the MVP to provide value without requiring centralized storage.
The extension layer can support productivity where the workflow happens, while keeping the user in control.
JSON export/import keeps the system portable and makes it easier to integrate with dashboards, future SaaS features or user-controlled backups.
AI adds value for coaching and analysis, but the core workflow should remain usable without AI dependencies.
This case study demonstrates practical experience with:
- Product architecture
- Browser extension architecture
- Next.js dashboards
- Local-first software design
- Privacy-first UX
- TypeScript contracts
- Workflow automation concepts
- AI-assisted product features
- Technical documentation
- Add richer job match scoring
- Add interview preparation flows
- Add dashboard filters and saved views
- Add optional encrypted backup
- Add stronger analytics for application conversion
- Add public demo mode with sample data
- Add case study screenshots and demo video
This repository is a public case study for portfolio and technical communication purposes.
The goal is to document product reasoning, system design and engineering maturity while keeping private implementation details protected.
Created by Gustavo Marques de Lima as part of the DevFlow Labs product ecosystem.
- Portfolio: https://devflowlabs.com.br
- GitHub: https://github.com/gustavomarques00
- DevFlow Labs GitHub: https://github.com/devflow-modules
- LinkedIn: https://www.linkedin.com/in/gustavo-marques-00