My words, GK Automation (Guru Krupa exports)

Client goes into Jewllery exhibitions, client have 10,000+ Jewellery designs and all are set inside different trays in their exhibitions, as of now they are doing this manual process where they have to assign a person with the visitor inexpo to not down what orders are they placing and this is taking too much time, Client want this to be automated. Then We as Dmind AI step in and made this automated by capturing photo of tray after they have stick pink color stickers on which jewellery do they want to purchase. In backend we have dataset of which tray holds which number of item code and all and we have tray number on in the image the expo visitor will provide so we're fetching their selections from datasets directly reducing their manual work.

== == == == == == == == # GK AUTOMATION — AI JEWELLERY EXPO ORDER CAPTURE SYSTEM

Project Overview

The GK Automation AI Jewellery Expo Order Capture System is an AI-powered exhibition order processing platform developed for Guru Krupa Exports to automate jewellery selection and order collection workflows during jewellery exhibitions and expos.

The client showcases:

• 10,000+ jewellery designs
• organized across multiple trays during exhibitions.

Previously, the client relied on a completely manual process where:

• a staff member was assigned to every visitor,
• manually noted down selected jewellery items,
• tracked tray references,
• and processed orders manually.

This workflow was:

• time-consuming,
• operationally expensive,
• prone to human error,
• and inefficient during high visitor traffic.

Dmind AI automated this process by introducing an AI-powered image recognition and order extraction system where:

• visitors simply place pink stickers on the jewellery pieces they want to purchase,
• a photo of the tray is captured,
• and the system automatically detects the selected products using tray intelligence and backend datasets.

The platform instantly converts selections into structured order data, eliminating manual note-taking and dramatically improving operational efficiency.

Project Objectives

Primary Goals

• Automate jewellery order capture during exhibitions
• Eliminate manual order note-taking
• Improve expo operational efficiency
• Reduce dependency on staff assistance
• Speed up customer order processing
• Improve order accuracy
• Digitize tray & inventory intelligence
• Create scalable exhibition automation infrastructure
• Simplify high-volume jewellery selection workflows

Core Modules

1. Jewellery Tray Intelligence System

Centralized tray mapping and inventory intelligence infrastructure.

Features

• Tray number mapping
• Jewellery item indexing
• Backend inventory datasets
• Item code association
• Tray-based product organization
• Product-to-position mapping

Dataset Structure

The backend system maintains:

• tray numbers,
• jewellery positions,
• item codes,
• product metadata,
• and inventory relationships

for all jewellery designs displayed during exhibitions.

2. AI Image Recognition & Selection Detection Engine

Computer vision system for detecting customer-selected jewellery items.

Features

• Tray image processing
• Pink sticker detection
• Jewellery position recognition
• Selection extraction
• Automated item identification
• AI-powered visual analysis

Workflow

1. Visitor places pink stickers on selected jewellery items 2. Staff captures tray image 3. AI analyzes tray image 4. System detects sticker positions 5. Backend maps positions to item codes 6. Final order data generated automatically

3. Automated Order Generation System

Order extraction and structured data processing infrastructure.

Features

• Automatic order creation
• Product code generation
• Selection data extraction
• Structured order formatting
• Bulk order handling
• Real-time order processing

Benefits

• Eliminates manual order writing
• Faster customer handling
• Reduced operational delays
• Improved order accuracy

4. Exhibition Workflow Automation Layer

Operational automation system designed specifically for jewellery expos and exhibitions.

Features

• High-volume visitor handling
• Fast tray scanning workflows
• Instant selection processing
• Real-time order generation
• Multi-tray support
• Queue-free customer workflows

Operational Benefits

• Faster visitor servicing
• Reduced staffing dependency
• Improved expo scalability
• Better customer experience

5. Backend Inventory & Dataset Engine

Core product intelligence infrastructure.

Features

• Jewellery dataset management
• Item code mapping
• Tray intelligence database
• Inventory synchronization
• Product lookup systems
• Structured product indexing

Data Managed

• Item codes
• Tray numbers
• Product positions
• Product details
• Collection mapping

Operational Transformation

Before Automation

Challenges

• Manual order note-taking
• Staff assigned to each visitor
• Slower customer handling
• High operational dependency
• Human errors during order recording
• Difficult handling of large exhibitions

After Automation

Results

• AI-powered order capture
• Automated jewellery selection detection
• Faster customer processing
• Reduced manual work
• Improved order accuracy
• Scalable exhibition workflows
• Better operational efficiency

Dashboard & Analytics System

6. Expo Monitoring Dashboard

Centralized operational analytics platform.

Metrics

• Orders captured
• Tray processing count
• Visitor handling efficiency
• Most selected products
• Order processing speed
• Exhibition performance analytics

Security & Reliability

Security Features

• Secure dataset management
• Role-based access control
• Protected inventory data
• Audit logging
• Secure image storage
• Backup systems

Key Features Summary

Main Highlights

• AI-powered jewellery selection detection
• Automated expo order capture
• Tray intelligence system
• Pink sticker recognition workflow
• Backend inventory mapping
• Automated item code extraction
• Exhibition workflow automation
• Faster visitor processing
• Reduced manual operational dependency
• Scalable jewellery expo infrastructure

Recommended Technology Stack

Frontend

• Next.js
• React
• Tailwind CSS

Backend

• Python
• FastAPI
• Node.js

AI & Computer Vision

• OpenCV
• YOLO
• TensorFlow
• Custom object detection models

Database

• PostgreSQL
• MongoDB

Storage & Infrastructure

• AWS S3
• Cloudflare R2
• Docker
• AWS

Development Roadmap

Phase 1

Tray Dataset Mapping & Workflow Analysis

Phase 2

Image Recognition & Sticker Detection

Phase 3

Automated Order Extraction System

Phase 4

Dashboard & Analytics Infrastructure

Phase 5

Scaling & Advanced AI Optimization

Final Vision

The GK Automation AI Jewellery Expo Order Capture System is designed to become a next-generation jewellery exhibition automation platform that combines:

• computer vision,
• AI-powered selection detection,
• inventory intelligence,
• automated order generation,
• and exhibition workflow automation

into one centralized operational ecosystem.

The ultimate goal is to help Guru Krupa Exports:

• process exhibition orders significantly faster,
• reduce manual operational workload,
• improve order accuracy,
• handle larger visitor volumes efficiently,
• and modernize jewellery exhibition operations

through intelligent AI-powered automation and visual recognition systems.