Vision Inspect

messaging feature(admin and inspector)
role based login
vision inspect ai
inspector dashboard
admin dashboard
report(admin)
defect detection(inspector)
report(inspector)

Inspiration

Product inspection often involves checking product images, identifying possible issues, recording inspection results, preparing reports, and communicating findings for review. When these activities are handled separately, the process can become time-consuming and difficult to organize.

This inspired us to build Vision Inspect, an AI-assisted product inspection and reporting application that brings the inspection workflow into one structured system. The project was developed as a continuation of our existing work under Pathway A, with the goal of reducing manual effort and making inspection and review more organized.

What We Built

Vision Inspect provides separate workflows for Admin and Inspector users.

The Admin can manage Inspector access, approve Inspectors, review submitted inspection reports, and communicate with Inspectors. An Inspector can log in after approval, provide a product image, perform AI-assisted inspection, review the generated result, and submit the inspection for Admin review.

The application also includes Admin–Inspector messaging so that inspection-related questions and clarifications can be handled within the same system.

The updated inspection workflow is:

Product Image → Image Pre-processing → Image Quality Check → Gemini AI Inspection → Structured Inspection Result → Report → Admin Review

The computer-vision preprocessing stage is used to validate and prepare the uploaded image before AI inspection. The AI-assisted result can include the identified issue, category, severity, explanation, and recommended action when these can reasonably be determined from the provided input.

How We Built It

Vision Inspect was developed using Google AI Studio with the existing application architecture based on React, TypeScript, Vite, Node.js, Express, Firebase, and API-based communication.

Google Gemini AI is integrated into the actual inspection workflow rather than being used only as a demonstration. The Inspector provides the required product/image input, the image is processed and validated, and the AI service generates the inspection response.

The application connects the AI response with the inspection result and reporting workflow so that the Admin can review the complete inspection information.

We also focused on maintaining role-based access so that an Inspector must receive Admin approval before accessing the inspection functionality.

What We Learned

Through this project, we learned how to integrate AI into an actual application workflow instead of treating AI as an isolated feature. We gained practical experience in connecting frontend workflows, backend/API functionality, application data, image processing, AI services, authentication, role-based access, reporting, and user communication.

We also learned that AI-generated results should not automatically be treated as ground truth. Image quality, input conditions, AI responses, processing behaviour, and complete application workflows need to be tested and validated.

Challenges We Faced

One of the main challenges was connecting AI inspection with the existing application without breaking the previously implemented Admin, Inspector, reporting, and messaging workflows.

Another challenge was handling image input reliably. Poor-quality or unsuitable images can affect AI-assisted inspection, so adding an image preprocessing and quality-check stage helped make the workflow more structured.

We also had to consider how AI results should be presented to users. Instead of showing an unstructured AI response, the application organizes the inspection information so that it can be understood and reviewed by the Admin.

Final Outcome

Vision Inspect evolved into a complete AI-assisted product inspection workflow combining computer-vision preprocessing, Gemini AI inspection, structured results, reporting, role-based access, Admin review, and communication.

The project demonstrates how AI and computer vision can be integrated into a practical inspection workflow to reduce manual effort and provide a more organized process from image submission to final administrative review.