ResumeAI

ResumeAI is an AI-powered career assistant designed to help users improve their job application process.

The platform analyzes uploaded resumes, identifies areas for improvement, extracts relevant skills, searches for matching job opportunities, and generates optimized application materials.

Rather than relying on a single AI prompt, the system uses multiple specialized AI workflows that work together to process documents, reason about improvements, and generate personalized outputs.

The project explores how agent-based AI systems can automate complex knowledge workflows.

Creating strong job applications is a time-consuming process. Candidates often struggle with:

• Understanding why their resume is not performing well
• Identifying missing skills or keywords
• Finding relevant opportunities
• Tailoring applications for different roles

Many existing tools provide basic resume templates or generic suggestions, but they do not combine resume analysis, job discovery, and personalized optimization into one workflow.

The goal was to create an AI assistant that could help users move from a generic resume to a more targeted application.

The system follows a multi-agent AI workflow: resume upload → document processing → AI analysis agents → job search integration → resume optimization → generated application materials.

Frontend

React — Provides the user interface for uploading resumes, viewing recommendations, and interacting with generated outputs.

Backend

FastAPI — Handles resume processing, API communication, AI workflow execution, and user requests.

AI Orchestration

LangGraph — Used to coordinate multiple AI agents with different responsibilities: resume analysis agent, skill extraction agent, job matching agent, and optimization agent. This creates a structured workflow instead of a single AI response.

External Services

Groq — Provides fast LLM inference for AI processing.

SerpAPI — Used to retrieve relevant job opportunities.

Firebase — Handles application services and user-related data.

Document Generation

ReportLab — Used to generate optimized resume documents.

Deployment

Docker — Used to package the application environment and improve deployment consistency.

AI Resume Analysis

Analyzes uploaded resumes to identify strengths, weak areas, missing keywords, and improvement opportunities.

Automated Job Matching

Searches for relevant job opportunities based on resume information and user goals.

AI Resume Optimization

Generates tailored resume versions designed around specific job requirements.

Multi-Agent Workflow

Different AI agents handle different parts of the process, creating a more structured and reliable pipeline.

Document Generation

Produces downloadable resume documents after optimization.

Designing Reliable AI Workflows

A major challenge was moving beyond simple prompting and creating a structured multi-step AI system. The workflow needed to coordinate multiple tasks while maintaining context between different stages.

Processing Unstructured Documents

Resumes contain information in many different formats. The system required extracting useful information from uploaded documents and transforming it into structured data that AI systems could process.

Connecting AI With External Systems

The project combined multiple services: language models, job search APIs, document generation, and backend APIs. Creating a reliable flow between these components required careful handling of failures, data formats, and user experience.

This project was an early exploration into building AI-powered products rather than isolated AI features.

The biggest lesson was that useful AI systems require strong engineering around the model. The model itself is only one part of the product. The surrounding system—workflow design, user experience, data handling, and reliability—determines whether the AI actually creates value.

This project influenced my later approach to building larger AI systems such as Tablo Study.