Dmind AI
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Project Detail

GK Maulik image identification for tray

Partner Client

Guru Krupa Exports

Client Domain

Jewellery, Diamonds, Export

Delivery Year

2024

Key Domain

Automation & Workflow Systems

GK Maulik image identification for tray

Technical Blueprint

System Integrations

The structural taxonomies and technology stacks designed for this custom operational deployment.

Initiate Engagement
Core Taxonomies
Automation & Workflow SystemsBranding & Experience PlatformsBusiness & Enterprise SoftwareComputer Vision & Image AIERP & Operations SystemsGen AI & AI Agents
Stack Expertise
Node.jsNestJSExpress.jsPythonChart.jsWebSocketsTimescaleDBRedisBullMQRAG ArchitectureDockerPostgreSQLCloudflareTypeScriptRechartsTailwind CSSAWSReactNext.jsMongoDBShadCN UIKubernetesFastAPI

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 mappingJewellery item indexingBackend inventory datasetsItem code associationTray-based product organizationProduct-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 processingPink sticker detectionJewellery position recognitionSelection extractionAutomated item identificationAI-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 creationProduct code generationSelection data extractionStructured order formattingBulk order handlingReal-time order processing

Benefits

Eliminates manual order writingFaster customer handlingReduced operational delaysImproved order accuracy

4. Exhibition Workflow Automation Layer

Operational automation system designed specifically for jewellery expos and exhibitions.

Features

High-volume visitor handlingFast tray scanning workflowsInstant selection processingReal-time order generationMulti-tray supportQueue-free customer workflows

Operational Benefits

Faster visitor servicingReduced staffing dependencyImproved expo scalabilityBetter customer experience

5. Backend Inventory & Dataset Engine

Core product intelligence infrastructure.

Features

Jewellery dataset managementItem code mappingTray intelligence databaseInventory synchronizationProduct lookup systemsStructured product indexing

Data Managed

Item codesTray numbersProduct positionsProduct detailsCollection 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 capturedTray processing countVisitor handling efficiencyMost selected productsOrder processing speedExhibition performance analytics

Security & Reliability

Security Features

Secure dataset managementRole-based access controlProtected inventory dataAudit loggingSecure image storageBackup 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

Strategic Path

Development Roadmap

Phase 1

Phase 1 — Tray Dataset Mapping & Workflow Analysis

Phase 2

Phase 2 — Image Recognition & Sticker Detection

Phase 3

Phase 3 — Automated Order Extraction System

Phase 4

Phase 4 — Dashboard & Analytics Infrastructure

Phase 5

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.

Implemented Tech Stack

Frontend

Next.jsReactTypeScriptTailwind CSSTradingView ChartsRechartsChart.jsShadCN UI

Backend

Node.jsPythonFastAPINestJSExpress.js

Real-time Infrastructure

WebSocketsRedis StreamsKafkaEvent-driven ArchitectureReal-time Data Pipelines

Market Data Infrastructure

NSE APIsBroker APIsTrading Data ProvidersTick-by-tick Market FeedsOptions Chain APIs

Trading Analytics Engine

Custom Formula EngineStrategy Execution SystemsSignal Processing InfrastructureDerivative Analytics

Options Intelligence

Open Interest AnalyticsGreeks MonitoringPCR AnalyticsMax Pain AnalysisIV AnalysisStrike Price Tracking

Database

PostgreSQLTimescaleDBRedisMongoDB

Trading Visualization Systems

TradingView IntegrationInteractive ChartsHeatmapsReal-time DashboardsIndicator Overlay Systems

Alerts & Notifications

Telegram AlertsDiscord NotificationsEmail AlertsPush Notification SystemsTrigger-based Alerts

AI & Automation

AI Trading InsightsPredictive AnalyticsSignal IntelligenceAutomated Market MonitoringStrategy Optimization

Performance Infrastructure

Low-latency SystemsMulti-user SynchronizationHigh-frequency Data ProcessingStreaming Architectures

Cloud & Infrastructure

AWSDockerKubernetesCloudflareCDN Infrastructure

Security & Authentication

JWT AuthenticationRBAC (Role-Based Access Control)Secure API IntegrationsEncrypted Data Transmission

Data Engineering

ETL PipelinesMarket Data Processing EnginesHistorical Data StorageAnalytics Pipelines

Additional Technologies

BullMQAPI Gateway SystemsMonitoring InfrastructureLogging SystemsFinancial Calculation Engines

Best-Fit Architecture

Next.js Trading Dashboard FrontendPython Analytics EngineNode.js API LayerTimescaleDB Time-series DatabaseRedis Streaming LayerKafka Event InfrastructureTradingView Visualization Layer

Complexity Level

Enterprise-Level Real-time F&O Trading Analytics Platform

Suggested Future Expansion

AI Trading CopilotAutomated Strategy BacktestingMulti-broker Trading IntegrationsAlgorithmic Trading InfrastructureAI Volatility ForecastingPortfolio Risk IntelligenceMobile Trading Analytics AppInstitutional-grade Trading APIsMulti-market Analytics Ecosystem

Industry Focus

Indian F&O TradingFinancial AnalyticsTrading IntelligenceReal-time Market MonitoringDerivatives AnalyticsQuantitative Trading SystemsFinancial Data EngineeringTrading Automation
GK Maulik engraving project