Building CareerCraft Studio
How I architected an AI-powered resume platform that understands your career story and tailors it for each opportunity
As a developer, I've always been frustrated by the "spray and pray" approach to job applications. Generic resumes that don't match job requirements, hours spent manually tailoring each application, and the constant worry about ATS compatibility. What if I could build something that actually understands your career story and intelligently adapts it for each opportunity?
That question led me to build CareerCraft Studio – an AI-powered platform that transforms how professionals create and manage their career documents. But this isn't just another resume builder. It's a showcase of modern AI-first architecture, featuring specialized agents, end-to-end type safety, and intelligent data processing.
The Problem I Set Out to Solve
- • Generic resumes that don't match job requirements
- • Time-intensive manual tailoring for each application
- • Poor ATS compatibility and keyword optimization
- • Difficulty organizing and optimizing career achievements
- • Complex data relationships (skills, work history, achievements)
- • Real-time AI interactions with proper state management
- • Intelligent parsing and normalization of unstructured data
- • Multi-industry skill categorization and matching
Architecture Overview: The Tech Stack That Made It Possible
The AI Agent Architecture: Building a Team of Specialists
The heart of CareerCraft Studio is its agent-based architecture. Instead of a monolithic AI system, I built a team of specialized agents that work together to handle different aspects of career management.
// Simplified example of the agent routing system
const supervisorAgent = {
route_to_agent: (userIntent) => {
if (userIntent.includes("add work experience")) return "data_manager";
if (userIntent.includes("generate resume")) return "resume_generator";
if (userIntent.includes("analyze job posting")) return "job_posting_manager";
// ... intelligent routing logic
}
};
Specialized Agents
- Data Manager Agent: Handles CRUD operations and resume parsing
- Resume Generator Agent: Creates tailored resumes based on job requirements
- Cover Letter Generator Agent: Generates personalized cover letters
- Job Posting Manager Agent: Analyzes job requirements and compatibility
- User Profile Agent: Provides insights about stored data
The Power of LangGraph StateGraph
- • Cyclical agent interactions
- • Complex workflow orchestration
- • State persistence across agent handoffs
- • Conditional routing based on conversation context
- • Error handling and recovery mechanisms
Technical Deep Dives
// Example of the intelligent parsing system
class ResumeParsingService {
async parseResume(resumeText: string, userId: string) {
// AI extraction of structured data
const extractedData = await this.llm.extract(resumeText);
// Batch database operations for performance
await this.db.transaction(async (tx) => {
await this.createWorkHistory(tx, extractedData.workHistory);
await this.createSkills(tx, extractedData.skills);
await this.createAchievements(tx, extractedData.achievements);
});
// Intelligent skill categorization
await this.normalizeSkills(userId);
}
}
enum SkillCategory {
PROGRAMMING_LANGUAGE,
MEDICAL_PROCEDURE,
FINANCIAL_ANALYSIS,
LEGAL_RESEARCH,
MARKETING_STRATEGY,
// ... 30+ categories across industries
}
// "React (hooks, context)" vs "React" - same skill, different detail levels
// End-to-end type safety from DB to UI
const trpcClient = api.chat.useSubscription({
onData: (aiResponse) => {
// TypeScript knows the exact shape of aiResponse
// Autocomplete works perfectly here
console.log(aiResponse.content, aiResponse.metadata);
},
onError: (error) => {
// Proper error handling with typed errors
}
});
The Database Design: Modeling Complex Career Data
- • Users → Work History → Achievements → Skills
- • Job Postings → Required Skills → User Skill Matching
- • Documents → Generated Content → Version History
- • Skills → Categories → Industry Mappings
- • Batch operations for resume imports
- • Intelligent skill deduplication algorithms
- • Optimized compatibility scoring queries
- • Efficient many-to-many relationship handling
Real-World Features That Showcase the Architecture
Users can chat naturally: "Add my job at Tech Corp where I built React apps and led a team of 5 developers."
- • AI extracts structured data and updates profile automatically
- • Context-aware follow-up questions for missing details
- • Intelligent merging with existing work history
Upload a job posting and get instant analysis of how well your profile matches.
- • AI extracts requirements from job descriptions
- • Real-time skill matching with gap analysis
- • Tailored resume generation highlighting relevant experience
- • Compatibility scoring across multiple dimensions
Intelligent organization and optimization of career achievements.
- • AI-powered merging of similar achievements
- • Smart categorization and impact quantification
- • Batch editing with transaction safety
- • Context-aware achievement suggestions
Development Experience & Lessons Learned
- • tRPC + Prisma: Incredible developer velocity
- • LangChain + TypeScript: Complex AI workflows with type safety
- • Next.js App Router: Server components + client interactions
- • Comprehensive docs: Every feature documented for AI and humans
- • AI unpredictability: Extensive error handling and fallbacks
- • Complex state management: LangGraph for agent orchestration
- • Performance at scale: Batch operations and intelligent caching
- • Multi-industry support: Flexible skill categorization system
- • Feature-based router organization (26KB+ files → focused modules)
- • Shared types and utilities across agents
- • Comprehensive tooling infrastructure
- • Clear separation between AI logic and business logic
The Impact: Real Developer and User Value
- • 10x faster resume creation
- • 95% ATS compatibility across major systems
- • Intelligent job matching across 50+ industries
- • Conversational interface that understands context
- • Type-safe full-stack development patterns
- • AI-first architecture examples and best practices
- • Comprehensive feature docs for rapid onboarding
- • Scalable agent patterns for complex workflows
Looking Forward: What's Next
- • Enhanced AI reasoning capabilities
- • Multi-modal document processing
- • Advanced analytics and career insights
- • Real-time collaboration features
- • Extracting reusable AI agent patterns
- • Sharing skill normalization algorithms
- • Community-driven feature development
- • Educational resources for AI-first development
Conclusion: Building AI-First Applications
- Modern TypeScript tooling enables rapid AI application development
- Agent-based architectures provide flexible and maintainable AI systems
- End-to-end type safety is crucial for complex AI workflows
- Comprehensive documentation amplifies both AI and human developer productivity
The future is AI-first applications built with traditional web development patterns. TypeScript and modern tooling make complex AI systems approachable, while focusing on real user problems ensures we're building something valuable.
The key is to start with the user problem, then let modern tools handle the complexity. CareerCraft Studio proves that individual developers can build sophisticated AI applications that compete with well-funded startups.
Want to see CareerCraft Studio in action or dive deeper into the technical implementation?