Jobcopilot
AI Job Search & Resume Platform
An AI copilot for job seekers that tailors résumés to each role and keeps the search organized.
- Category
- SaaS
- Type
- Product
- Status
- pre-production testing
01 · Overview
The problem and the solution
Business problem
Job seekers spend hours tailoring résumés and cover letters for each application and tracking dozens of opportunities across sites, with little structure or feedback.
Solution
Jobcopilot pairs AI-assisted résumé tailoring with a focused application tracker, helping candidates present relevant experience for each role and stay on top of their search.
02 · Functional
What the product does
Key features
Résumé tailoring
Align résumé content with the requirements of a specific job description.
Application tracking
Follow every application from saved to offer in one place.
AI-assisted cover letters
Draft role-specific cover letters to review and refine.
Job fit insights
Highlight how a role matches the candidate's experience.
User workflows
- The candidate uploads a base résumé and sets target roles.
- For each job description, Jobcopilot highlights the most relevant experience and suggests tailored wording.
- The candidate reviews and edits the tailored résumé and an AI-drafted cover letter.
- Each application is tracked from saved to applied, interviewing, and offer.
03 · Technical
How it's built
Technology stack
- Next.js
- React
- TypeScript
- Node.js
- PostgreSQL
- OpenAI
System architecture
Jobcopilot is a Next.js application with a TypeScript API and PostgreSQL. Résumés are stored as structured data — roles, achievements, and skills — rather than plain text, which lets the tailoring step reorder and rephrase content without losing facts.
LLM calls are wrapped in a service layer with prompt templates, schema-validated output, and guardrails that keep generated content tied to the candidate's real experience.
Engineering highlights
- Structured résumé model that separates facts (roles, dates, results) from wording.
- Tailoring prompts constrained to the candidate's own experience, with validated output.
- Side-by-side comparison so candidates see exactly what changed.
- Application tracker with clear status stages.
Challenges and design decisions
- Accuracy over polish: generated text must never invent experience, so outputs are checked against the source résumé.
- Keeping the candidate in control: every suggestion is editable and nothing is sent automatically.
- Clean exports: producing well-formatted tailored résumés required a dedicated rendering step.
04 · Infrastructure
Hosting, delivery, and operations
- The Next.js application runs on a managed hosting platform, with PostgreSQL on a managed provider and automated backups.
- LLM requests go through a server-side service layer with rate limits and timeouts; API keys never reach the browser.
- Résumé files are stored privately in object storage and served through short-lived signed URLs.
- CI/CD with a preview deployment for every change, plus type checks and tests.
- Error tracking and usage monitoring for AI features, including token usage per request.
Interested in Jobcopilot?
Visit the product website, or get in touch for a walkthrough or a technical discussion.