{"slug": "i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats", "title": "I Built a Multi-Provider AI Resume Analyzer with Spring Boot — Here's How ATS Matching Works", "summary": "A developer built ResumeIQ AI, a Java 17 and Spring Boot 3 application that scores resumes against specific job descriptions rather than producing a generic ATS number. The system supports multiple AI providers — OpenAI, Gemini and Ollama — and returns an overall match score, ATS compatibility, matching and missing skills, keyword frequency analysis and improvement suggestions, with an example run showing an 88% match and 90% ATS compatibility.", "body_md": "If you've ever tried building an AI-powered resume analyzer, one of the first things you discover is that:\n\n\"Send the resume to an AI model and ask for an ATS score\" isn't really enough.\n\nA useful resume-analysis system needs to answer several questions:\n\nHow well does the resume match a specific job description?\n\nWhich skills are missing?\n\nWhich keywords are missing or underrepresented?\n\nHow strong is the ATS compatibility?\n\nDoes the candidate's experience match the role?\n\nWhat could be improved?\n\nCan AI help rewrite the resume?\n\nCan the system compare different resume versions?\n\nI wanted to explore these problems from a backend-engineering perspective, so I built ResumeIQ AI, a Java and Spring Boot based AI resume-analysis application.\n\nThe interesting part wasn't simply connecting an application to an AI API.\n\nIt was building the workflow around it.\n\nThe Technology Stack\n\nThe backend is built around:\n\nJava 17\n\nSpring Boot 3\n\nSpring Security\n\nSpring Data JPA\n\nMySQL\n\nThymeleaf\n\nMaven\n\nOpenAI\n\nGemini\n\nOllama\n\nOne of the design decisions I wanted to explore was multi-provider AI support, rather than coupling the application to a single AI provider.\n\nAn ATS score without a target job isn't particularly meaningful.\n\nConsider a resume containing:\n\nJava\n\nSpring Boot\n\nMySQL\n\nDocker\n\nREST APIs\n\nMicroservices\n\nFor one position, that could be a strong match.\n\nFor another position requiring:\n\nJava\n\nSpring Boot\n\nKafka\n\nAWS\n\nKubernetes\n\nRedis\n\nthe same resume could have significant gaps.\n\nSo instead of asking:\n\n\"Is this a good resume?\"\n\nthe application asks:\n\n\"How well does this resume match this particular job?\"\n\nThe basic workflow is:\n\nResume\n\n   +\n\nJob Description\n\n   ↓\n\nResume Analysis\n\n   ↓\n\nMatching\n\n   ↓\n\nATS Score\n\n   ↓\n\nSkills + Keywords + Gaps\n\n   ↓\n\nAI Suggestions\n\n   ↓\n\nResume Improvement\n\nHere's what the analysis starts with:\n\nResumeIQ starts with a job description and resume rather than generating a generic resume score.\n\nThe user can provide a resume and the complete job description, and the application analyzes the two together.\n\nThe matching process can be thought of as:\n\nResume\n\n   ↓\n\nExtract relevant skills / experience / keywords\n\n   ↓\n\nJob Description\n\n   ↓\n\nExtract required skills / keywords\n\n   ↓\n\nCompare\n\n   ↓\n\nGenerate analysis\n\nThe result isn't intended to be just one number.\n\nIt can contain:\n\nOverall match score\n\nATS compatibility\n\nMatching skills\n\nMissing skills\n\nKeyword analysis\n\nExperience comparison\n\nScore breakdown\n\nImprovement suggestions\n\nHere's an example from the application:\n\nThe analysis provides both an overall match score and a more detailed breakdown of the resume against the job description.\n\nThe example shows an 88% match along with an ATS compatibility score of 90%.\n\nThe important part is that the score is accompanied by additional information explaining what contributed to it.\n\nA single score can be difficult to interpret.\n\nFor that reason, ResumeIQ breaks the analysis into different categories.\n\nFor example:\n\nTechnical Skills\n\nExperience\n\nProjects\n\nEducation\n\nAchievements\n\nEach category contributes to the overall analysis.\n\nThe score is broken into multiple categories rather than presenting only a single ATS number.\n\nThis makes it easier to identify where the resume is relatively strong and where it needs improvement.\n\nOne of the more useful parts of resume-to-job matching is identifying what is missing.\n\nFor example, a job description might require:\n\nJava\n\nSpring Boot\n\nHibernate\n\nPostgreSQL\n\nMicroservices\n\nAWS\n\nCI/CD\n\nwhile the resume may clearly demonstrate some of those skills but not others.\n\nResumeIQ separates this information into areas such as:\n\nMatching skills\n\nMissing skills\n\nKeyword frequency\n\nKeyword recommendations\n\nImportance of missing skills\n\nThe application identifies missing skills and also analyzes how frequently important keywords appear in the resume.\n\nThis is more actionable than simply telling someone:\n\n\"Your ATS score is 78.\"\n\nThe more useful question is:\n\n\"Why is the score 78, and what can I change?\"\n\nTraditional keyword matching has limitations.\n\nSuppose a resume contains:\n\n\"Built backend services using Spring Boot.\"\n\nwhile a job description says:\n\n\"Experience developing RESTful microservices using Spring Boot.\"\n\nA useful system needs to consider more than whether two strings are identical.\n\nThis is where AI can become useful.\n\nThe application can use AI to interpret the resume and job-description context and generate recommendations around:\n\nwording\n\nexperience descriptions\n\nmissing information\n\nresume structure\n\npotential improvements\n\nThe goal isn't to let the LLM decide everything.\n\nInstead, the application combines structured analysis with AI-generated reasoning.\n\nAfter identifying potential weaknesses, the system can turn them into concrete recommendations.\n\nFor example, the analysis can suggest improvements around:\n\nAWS experience\n\nresume structure\n\nduplicate skills\n\nATS formatting\n\nexperience descriptions\n\nThe AI layer turns the analysis into specific suggestions instead of stopping at a score.\n\nThis creates a workflow closer to:\n\nAnalysis\n\n   ↓\n\nIdentify weakness\n\n   ↓\n\nExplain weakness\n\n   ↓\n\nSuggest improvement\n\nrather than simply:\n\nResume → Score → Done\n\nOne of the more practical features is showing how specific resume statements could be improved.\n\nBefore\n\nDeveloped and Maintained backend microservices and relational databases in production; reduced API response time by 30% through query optimization and service tuning.\n\nAfter\n\nDeveloped and maintained production backend microservices and relational databases using Spring Boot, reducing API response times by 30% via query optimization and database tuning.\n\nThe objective isn't to invent experience.\n\nIt's to make existing experience clearer and more aligned with the job requirements.\n\nThe application shows concrete before-and-after examples rather than only giving general writing advice.\n\nThe next step is taking those recommendations and generating a complete improved version.\n\nResumeIQ includes an AI resume-rewriting workflow that can generate an ATS-oriented version based on the selected job description.\n\nThe generated resume can then be reviewed and exported in different formats.\n\nThe important design principle here is that the system should improve how existing experience is presented rather than fabricate qualifications.\n\nResume optimization is often iterative.\n\nA candidate might have:\n\nVersion 1 → 82%\n\nVersion 2 → 88%\n\nVersion 3 → 92%\n\nInstead of treating every analysis as completely independent, ResumeIQ includes functionality around:\n\nanalysis history\n\nresume versions\n\ncomparing resumes\n\ntracking score changes\n\nThe application also supports comparing two resumes against the same job description.\n\nThis can be useful when experimenting with different versions of the same resume.\n\nOne architectural decision I wanted to explore was avoiding a hard dependency on one AI provider.\n\nResumeIQ supports:\n\nOpenAI\n\nGemini\n\nOllama\n\nConceptually:\n\n```\n             ┌── OpenAI\n             │\n```\n\nApplication ── AI Provider Layer ── Gemini\n\n                 │\n\n                 └── Ollama\n\nThis gives developers flexibility to experiment with different providers and deployment approaches.\n\nFor example, a developer might want to use a cloud model in one environment and a locally running model through Ollama in another.\n\nFor this project, Spring Boot acts as the backend foundation connecting the different parts of the application.\n\nThe application needs to handle:\n\nAuthentication\n\n      ↓\n\nResume processing\n\n      ↓\n\nJob-description processing\n\n      ↓\n\nMatching\n\n      ↓\n\nAI integration\n\n      ↓\n\nPersistence\n\n      ↓\n\nAnalysis results\n\nThis is where the project becomes more interesting than simply calling an AI API.\n\nThe backend has to coordinate multiple workflows and maintain the state of analyses, resumes and versions.\n\nThe most interesting part of building an AI application isn't necessarily:\n\n\"How do I call an LLM?\"\n\nThe harder questions are:\n\nWhat information should be sent to the model?\n\nHow much context is enough?\n\nWhich parts should be deterministic?\n\nWhich parts actually benefit from an LLM?\n\nHow do you avoid coupling the entire application to one provider?\n\nThese questions become increasingly important as an AI application grows beyond a simple API demo.\n\nThe resulting application includes functionality around:\n\nATS-style resume analysis\n\nJob-description matching\n\nMatch scoring\n\nMissing keywords\n\nMissing skills\n\nKeyword analysis\n\nExperience-gap detection\n\nImprovement suggestions\n\nAI rewriting\n\nResume/JD comparison\n\nResume versioning\n\nAnalysis history\n\nInterview preparation\n\nMultiple AI providers\n\nDashboard functionality\n\nThe project is designed as a source-code foundation that developers can customize and extend, rather than as a hosted SaaS product.\n\nThere are several areas I'd like to explore further.\n\nBetter semantic matching\n\nMoving beyond basic keyword matching toward deeper semantic comparison between resume experience and job requirements.\n\nBetter context selection\n\nDetermining which parts of a resume and job description actually need to be sent to the model.\n\nModel evaluation\n\nCreating a consistent way to compare different models and prompts for resume-analysis tasks.\n\nMore provider flexibility\n\nMaking it easier to switch between cloud-based and locally hosted models.\n\nMore career workflows\n\nExpanding the system beyond resume analysis into additional parts of the job-search workflow.\n\nFinal Thoughts\n\nBuilding ResumeIQ changed how I think about AI applications.\n\nThe interesting part isn't:\n\nResume → LLM → Score\n\nIt's the system around the model:\n\nStructured Analysis\n\n       +\n\nMatching\n\n       +\n\nAI Reasoning\n\n       +\n\nActionable Recommendations\n\n       +\n\nResume Improvement\n\nAI becomes much more useful when it is combined with application logic instead of being treated as the entire application.\n\nFor me, the combination of:\n\nJava + Spring Boot + AI + document processing + matching + real-world automation\n\nis what makes this project interesting from a backend-engineering perspective.\n\nSource Code\n\nIf you're building a resume platform, career SaaS, ATS tool, or experimenting with AI-powered career applications, I made the complete ResumeIQ AI source code available as a customizable Spring Boot foundation.\n\nResumeIQ AI — AI Resume Analyzer & ATS SaaS Starter\n\n[https://javacoder716.gumroad.com/l/resumeiq-ai](https://javacoder716.gumroad.com/l/resumeiq-ai)", "url": "https://wpnews.pro/news/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats", "canonical_source": "https://dev.to/sweety717/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-heres-how-ats-matching-works-3jg5", "published_at": "2026-09-29 15:36:11+00:00", "updated_at": "2026-09-29 15:46:39.543454+00:00", "lang": "en", "topics": ["ai-tools", "large-language-models", "ai-products", "developer-tools"], "entities": ["ResumeIQ AI", "Spring Boot", "Java 17", "OpenAI", "Gemini", "Ollama", "MySQL", "Thymeleaf"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats", "markdown": "https://wpnews.pro/news/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats.md", "text": "https://wpnews.pro/news/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats.txt", "jsonld": "https://wpnews.pro/news/i-built-a-multi-provider-ai-resume-analyzer-with-spring-boot-here-s-how-ats.jsonld"}}