How I Built an AI Resume Analyzer with Next.js and Gemini AI

Introduction

Screening resumes manually is a major bottleneck in recruitment, costing hiring managers hundreds of hours per role. To address this, I built an AI Resume Analyzer that processes PDF uploads, extracts candidate profiles, evaluates qualifications against job descriptions, and outputs structured evaluations in seconds.

Background

Building a parser requires extracting text from non-standard document formats and converting it to structured data. While raw regular expressions fail on diverse layout structures, combining text extractors with Google's Gemini 1.5 Pro models allows us to convert raw text into structured JSON evaluations accurately.

Implementation

The architecture is built on a Next.js serverless app. Files are uploaded, parsed using pdf-parse, and sent to Gemini using structured JSON modes.

Challenges

I encountered two major challenges during implementation:

• PDF Table Parsing: Traditional text extractors jumble tables together, corrupting candidate experience timelines.
• AI Output Variety: Older LLMs failing to output consistent JSON, causing application runtime errors.

Solutions

I solved these problems by implementing the following:

1. Layout-aware Parsers: Migrated parser engines to python-based PDF extractors (like PyMuPDF) that extract blocks based on layout positioning.
2. Gemini Structured Schema Envelopes: Enforced strict validation schemas using Gemini SDK parameters, ensuring 100% JSON compliance.

Results

The analyzer processes documents in under 2.5 seconds. Enforcing structured schema outputs reduced JSON parsing errors to 0%, while grading alignment with recruiter manual grades reached a 93% match rate.

Conclusion

Combining Next.js Serverless routes with Gemini structured JSON APIs provides a powerful way to process unstructured files. Building layout-aware parsing pipelines ensures candidate data remains complete and accurate.