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Building Resumind: An AI Resume Analyzer
How I built Resumind, an AI-powered SaaS that analyzes resumes against job descriptions and provides actionable improvement suggestions.
21 min read·Talha Bilal
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Introduction
Applying for a job is not just about having a resume — it is about making sure that resume communicates the right experience, skills, and keywords for the position you're targeting.
I built Resumind to help solve that problem. It is an AI-powered resume analysis SaaS that evaluates a resume against a specific job description and identifies both the areas that are already strong and the areas that need improvement.
The system supports two analysis modes. The first focuses on identifying strengths and weaknesses in the resume. The second provides a more actionable analysis by suggesting how those weak areas can be improved, including relevant content and keywords that could be added to the resume.
The project also goes beyond the AI analysis itself. I built the surrounding SaaS infrastructure including authentication, resume processing, analysis history, credit-based usage, dashboard functionality, and a Stripe sandbox payment flow.
The application is currently live as a portfolio project at Resumind.talhabilal.dev.
The Problem
A resume can look strong on its own and still be a poor match for a particular job.
The problem is that most resume feedback is too generic. A candidate might know that their resume needs improvement, but not know which parts are weak for the specific position they are applying to, which skills or keywords are missing, or how their existing experience could be presented more effectively.
I wanted Resumind to approach the problem from a different angle: instead of evaluating a resume in isolation, the system compares it against the actual job description provided by the user.
This creates a more useful question:
"How well does my resume match this specific job, and what should I improve before applying?"
That became the core idea behind Resumind.
The application therefore needed to do more than generate a generic AI response. It needed to take an uploaded resume, extract its content reliably, understand the requirements of the target job, produce structured analysis, and present the results in a way that a user could actually act on.
This also introduced several engineering requirements beyond the AI model itself:
- Resume files needed to be converted into usable text before analysis.
- The AI response needed to follow a predictable structured format rather than returning arbitrary text.
- Users needed to consume analyses through a credit-based system.
- Previous analyses needed to be accessible through the application dashboard.
- The application needed authentication and a way to manage users and their data.
- A payment flow was needed to demonstrate how credits could be purchased.
The result was not simply an AI prompt wrapped in a web interface. Resumind became a small SaaS system built around an AI-powered analysis pipeline.
What I Built
Resumind takes a simple input: a resume and a target job description.
Instead of analyzing the resume independently, the system uses the job description as the context for the analysis. This allows the AI to evaluate how well the candidate's existing experience and skills align with the specific position they are targeting.
The application provides two analysis modes.
Standard Analysis
The standard analysis focuses on identifying what is already working and where the resume is weak in relation to the target job.
It gives the user a concise assessment of the resume so they can quickly understand where they stand before applying.
Full Analysis
The full analysis goes a step further by turning the evaluation into actionable recommendations.
It provides:
- Resume score — an overall assessment of how well the resume matches the target job.
- Strengths — areas where the resume already aligns well with the job requirements.
- Weaknesses — areas where the resume could be improved.
- Missing skills — relevant skills identified from the job description that are not sufficiently represented in the resume.
- Missing keywords — important terms from the job description that could improve the resume's relevance.
- Suggested content — example content that can be adapted and added to the resume.
- Bullet-point improvements — recommendations for making existing resume points clearer and more relevant to the target position.
The goal was to make the AI output useful beyond simply saying that a resume is "good" or "bad."
A score can tell a candidate where they stand, but actionable recommendations tell them what to do next.
From Upload to Analysis
The core user flow is intentionally straightforward:
- The user uploads their resume.
- The user provides the job description they are applying for.
- Resumind processes the uploaded document and extracts its text.
- The extracted resume content and job description are sent to the AI analysis layer.
- The AI returns a structured JSON response.
- The analysis consumes the appropriate number of credits.
- The results are stored and made available through the user's dashboard.
This separation between document processing, AI analysis, and the SaaS layer allowed me to keep the user experience simple while still having a clear technical pipeline behind it.
System Architecture
Resumind is built around a relatively simple request pipeline, but there are a few important pieces of logic around the AI call.
The frontend collects the resume and target job information. The resume is processed to extract its text, while the API validates the required inputs before starting the analysis.
Once the request passes validation, Resumind deducts the required credits and sends the resume content together with the job description to OpenAI. The AI returns a structured JSON response that the application can process and display consistently.
An important part of the flow is credit protection. Credits are deducted before the AI request, but if the analysis fails instead of returning a successful result, the consumed credits are refunded. This prevents users from losing credits because of an unsuccessful AI operation.
Rendering diagram...
Core Components
The application is divided into a few focused layers:
| Component | Responsibility |
|---|---|
| Next.js frontend | Resume upload, job description input, analysis interface, and dashboard |
| PDF parser | Extracts text from supported PDF documents |
| API routes | Validates requests, manages credits, and coordinates the analysis |
| OpenAI | Performs the resume and job-description analysis |
| MongoDB | Stores users, analyses, and application data |
| Stripe | Provides the sandbox payment flow used to demonstrate credit purchases |
This architecture keeps the AI model itself as one part of the system rather than making the entire application dependent on a single AI request.
Handling Failed AI Requests
One of the small but important implementation details is what happens when the AI request fails.
The application does not simply deduct credits and assume the operation will succeed. Instead, the credit transaction is treated as recoverable:
Deduct credits → run analysis → if the analysis fails, refund the credits.
This provides a better experience for users and prevents failed AI requests from unnecessarily consuming their available credits.
AI Analysis Pipeline
The core of Resumind is the analysis pipeline that turns an uploaded resume and a job description into structured, actionable feedback.
The pipeline starts before the AI model is called. Resumind first needs to turn the uploaded document into text that the model can understand.
1. Resume Text Extraction
Resumind currently supports text-based PDF documents.
When a user uploads a supported PDF, the document is processed and its text is extracted. This extracted content becomes the input for the analysis pipeline.
Image-based or scanned PDFs are currently not supported. Because these documents contain the resume as an image rather than selectable text, the parser cannot extract the content required for analysis.
2. Input Validation
After the resume content has been prepared, the application validates the analysis request.
The request needs the required resume content and job information before the analysis can proceed. This prevents incomplete requests from consuming AI credits unnecessarily.
The job description is especially important because Resumind is designed around job-specific analysis, rather than generic resume evaluation.
3. Credit Handling
Once the request passes validation, Resumind deducts the required credits for the selected analysis mode.
The current system uses:
- 5 credits for the standard analysis
- 10 credits for the full analysis
The credit deduction happens before the AI request. To protect the user from failed requests, credits are refunded if the analysis does not complete successfully.
4. AI Analysis
The extracted resume text and the target job description are then provided to OpenAI.
Rather than relying on an unstructured text response, Resumind expects the AI to return the analysis as structured JSON.
This makes the response easier for the application to process and allows individual parts of the analysis to be displayed in dedicated sections of the dashboard.
For the full analysis, the structured result includes information such as:
- Resume score
- Strengths
- Weaknesses
- Missing skills
- Missing keywords
- Suggested resume content
- Improved resume bullet points
The AI therefore acts as an analysis engine, while the application remains responsible for validating, storing, and presenting the result.
5. Persisting the Result
After a successful AI response is received, the analysis result is saved to MongoDB.
The user can then access the result through the dashboard rather than having the analysis exist only as a temporary API response.
This also allows Resumind to maintain an analysis history, giving users a way to return to previous resume evaluations.
The Complete Flow
Rendering diagram...
The important design principle here is that the AI call is only one stage of the workflow. Resumind combines document processing, validation, usage management, AI inference, failure recovery, persistence, and presentation into a single product flow.
Building the SaaS Layer
The AI analysis is the core feature of Resumind, but the project also needed the infrastructure around it to behave like an actual SaaS application.
A user needs to be able to create an account, run analyses, consume credits, view previous results, and interact with the application's billing system.
Authentication
Resumind uses custom authentication to manage user accounts and provide authenticated access to the application.
Authentication is important beyond simply protecting the dashboard. Each analysis and its associated usage need to belong to the correct user, so the application can maintain separate analysis history and credit balances.
Analysis History
Completed analyses are persisted in MongoDB rather than being discarded after the API request finishes.
The application currently uses five main models to organize its data:
| Model | Responsibility |
|---|---|
| User | Stores user account information |
| Resume | Stores uploaded resume-related data |
| JD Analysis | Stores job-description-based resume analysis results |
| Stripe | Stores payment-related information |
| Transaction | Tracks credit and payment transactions |
This separation keeps the core application data independent from payment and transaction records.
Credit-Based Usage
Resumind uses a credit-based model for AI analysis.
The current implementation charges:
- 5 credits for a standard analysis
- 10 credits for a full analysis
Credits provide a straightforward way to control AI usage while also creating a foundation for a paid SaaS model.
The credit system is also connected to the analysis pipeline. Credits are deducted before an AI analysis begins, and failed analyses trigger a refund so that users are not charged for an unsuccessful request.
Payment Integration
I also integrated Stripe into Resumind to build the payment and credit-purchasing flow.
The integration currently uses Stripe's sandbox/test environment rather than processing real payments. The purpose of this implementation is to demonstrate the complete payment architecture and how successful transactions can be connected to the application's credit system.
The payment flow can therefore be represented as:
Rendering diagram...
The payment integration was also one of the more challenging parts of the project for me because it was my first experience integrating a payment gateway. It required understanding the relationship between payment events, transactions, and the application's internal credit system rather than simply adding a payment button to the interface.
From AI Tool to SaaS Product
This infrastructure is what separates Resumind from a simple AI demo.
The AI model performs the analysis, but the application around it handles the parts required to turn that capability into a usable product:
Authentication → Credits → Analysis → Persistence → History → Payments
That was an important part of the project: building the product around the AI capability rather than treating the AI API as the entire application.
Technical Challenges & Solutions
Building Resumind involved more than connecting a frontend to an AI API. Some of the more interesting problems came from the parts surrounding the AI workflow, particularly document processing, payment verification, and protecting user credits.
1. Verifying Stripe Transactions
The payment integration was one of the most challenging parts of the project because it was my first time implementing a payment gateway.
One important lesson was that initiating a payment is not the same as treating it as a completed transaction.
Resumind needed to verify the transaction with Stripe before considering the payment successful and updating the application's internal payment and credit state.
The flow therefore became:
Rendering diagram...
The key takeaway was that the application's internal state should not blindly trust that a payment was completed simply because a payment request was initiated.
2. Extracting Text from PDF Resumes
Resumind depends on the actual content of a user's resume, so reliable PDF text extraction is an important part of the pipeline.
The challenge is that PDFs are not all structured in the same way. A text-based resume can contain selectable text that a parser can extract, while a scanned resume may effectively contain only an image.
Resumind currently supports text-based PDFs, but does not provide OCR processing for image-only or scanned PDFs.
This means the document-processing stage needs to produce usable resume text before the AI analysis can begin.
Rendering diagram...
This was an important practical constraint to account for rather than assuming every uploaded PDF would behave like a normal text document.
3. Protecting User Credits During Failed Analysis
Because AI analysis consumes credits, a failed request should not leave the user paying for an operation that never produced a result.
Resumind therefore treats credit consumption as part of the analysis workflow.
The application deducts the required credits before calling OpenAI. If the AI analysis does not return a successful result, the application refunds those credits.
text
1Validate request2 ↓3Deduct credits4 ↓5Run AI analysis6 ↓7 Success?8 ↙ ↘9 Yes No10 ↓ ↓11Save Refund12result creditsThis is a relatively small piece of application logic, but it has an important effect on the reliability of the product: a failed AI request does not unnecessarily consume the user's credits.
What These Challenges Taught Me
The biggest lesson from these problems was that integrating an external service is rarely just about making the first API call work.
The surrounding system needs to account for:
- Verification
- Failure states
- Data consistency
- User state
- External service responses
- Recovering from unsuccessful operations
That became particularly clear while integrating both Stripe and OpenAI into Resumind.
Structured AI Output
One challenge with integrating an LLM into an application is that the model's response needs to be predictable enough for the rest of the application to consume.
Resumind therefore does not treat the AI response as arbitrary text. The analysis is designed around a structured JSON response, with the expected shape defined using Zod schemas.
The workflow is essentially:
Rendering diagram...
This gives the application a clear contract between the AI layer and the rest of the system.
For example, the full analysis needs predictable fields for the score, strengths, weaknesses, missing skills, missing keywords, suggested content, and improved bullet points. Validating the response before storing or displaying it reduces the risk of malformed AI output breaking the application UI.
I used AI-assisted development to iterate on the prompts themselves, while defining the application's expected response structure and validation around those prompts. This reflects how I approach LLM features in practice: the model can generate the intelligence, but the application still needs to enforce structure and reliability around it.
Result & Current State
Resumind is currently deployed and available as a live portfolio project at Resumind.talhabilal.dev.
The finished application combines an AI-powered resume analysis workflow with the infrastructure needed to support it as a SaaS product.
A user can:
- Create and authenticate an account
- Upload a text-based PDF resume
- Provide a target job description
- Run a standard or full resume analysis
- Receive a job-specific resume score and feedback
- Identify strengths, weaknesses, missing skills, and missing keywords
- Get actionable suggestions for improving resume content
- Review previous analyses through the dashboard
- Consume analyses through a credit-based system
- Use the Stripe sandbox flow to demonstrate purchasing credits
The project is not currently being actively marketed. I built Resumind primarily as a portfolio project to demonstrate my ability to take an AI product from an idea to a deployed, usable SaaS application.
More importantly, the project gave me practical experience across several parts of modern application development at once: AI integration, document processing, authentication, database design, credit management, payment integration, and failure handling.
Future Improvements
ResumeMind is currently functional as a portfolio project, but there are several improvements planned for future iterations.
Credit & Usage System
- Separate the credit cost for JD/Resume Analysis and Full Resume Analysis.
- Add the third Improved Resume Content service.
- Set Improved Resume Content to consume 3 credits.
- Ensure each service has its own clearly defined credit cost.
- Update the dashboard so users can clearly see the credit cost before running an operation.
- Add appropriate credit handling and refunds for every AI-powered operation.
Resume Upload & PDF Processing
- Improve PDF validation before the document reaches the analysis API.
- Detect unsupported or invalid PDFs early and prevent them from entering the AI pipeline.
- Improve text extraction reliability for different PDF structures and layouts.
- Add support for scanned/image-based PDFs through OCR, or explicitly reject them with a clear user-facing validation message.
- Improve handling of malformed, empty, or unreadable resume documents.
Resume Generation & Download
- Add the ability to generate a downloadable resume from the improved resume content.
- Start with a single professionally designed resume template.
- Add additional resume templates after the initial template is stable.
- Allow users to choose between multiple resume templates.
- Charge credits for resume generation/download according to the finalized credit model.
- Ensure the generated resume is properly formatted and downloadable as a PDF.
Payments
- Replace the current Stripe sandbox payment flow with a production-compatible payment method.
- Investigate and integrate a payment provider that supports the target market.
- Evaluate NowPayments for cryptocurrency-based credit purchases.
- Implement production transaction verification.
- Ensure successful payments reliably update user credits.
- Ensure failed or incomplete payments do not add credits.
- Maintain transaction records for payment history and reconciliation.
Saved Jobs
- Allow users to save job descriptions inside ResumeMind.
- Allow users to give saved jobs a title/name.
- Allow users to reuse a saved job for future resume analyses.
- Allow users to manage saved jobs from their dashboard.
- Avoid requiring users to repeatedly paste the same job description when analyzing a resume for the same position.
AI & Analysis Improvements
- Improve prompt quality and analysis consistency.
- Add stronger validation around AI-generated responses.
- Improve the analysis pipeline to handle unexpected or incomplete AI responses.
- Evaluate newer/improved AI models when they provide meaningful quality or cost advantages.
- Expand resume-specific analysis capabilities.
- Add more actionable recommendations based on the target job.
- Continue improving the quality of generated resume content.
Product Improvements
- Expand the resume feature set beyond analysis.
- Improve the overall resume-building workflow.
- Improve the dashboard experience as more resume services are added.
- Make the relationship between credits, available services, and generated outputs clearer.
- Continue refining the product based on actual usage and user feedback.
Key Takeaways
-
AI features need application architecture around them. ResumeMind combines LLM analysis with document processing, validation, authentication, persistence, credits, and payments.
-
Structured AI output matters. Using Zod validation around the JSON response gives the application a predictable contract instead of relying on arbitrary LLM text.
-
External integrations need failure handling. Stripe transaction verification and AI credit refunds were important parts of making the application reliable.
-
Job-specific analysis is more actionable than generic resume feedback. Comparing the resume directly against the target job description allows ResumeMind to provide more relevant recommendations.
-
The project gave me experience building an AI product end-to-end. From the frontend and backend to AI integration, database design, payments, and deployment, ResumeMind was built as a complete SaaS application rather than an isolated AI demo.
Have questions about ResumeMind or want to discuss an AI product? Reach out through the contact page.
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