Backend Development Projects in Ghaziabad
Backend Development Projects in Ghaziabad
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AKASH VASHISHTHA
pro
AI Platform Stabilization and Enhancement for Kaie
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107
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1
Wahid Ali
pro
Flight Booking Platform
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23
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Satya Prakash
pro
Dream Planner - AI-Powered Goal Planning Platform
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44
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Hritwik Tripathi
pro
SnipeMe Clipping Tool Development
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4
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Himanshu Bansal
pro
TK-Coach - Wellness App
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11
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Krishna Poddar
Fluentify-app
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36
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Ritik Goyal
An enterprise SaaS platform that lets FMCG companies plan, simulate, and optimize trade promotions. It gave 15+ enterprise clients the ability to model 200+ promotional scenarios monthly, run what-if analysis through a Promotion Calendar Optimizer and Simulator, and forecast the impact of promotions on revenue, volume, and profitability across $50M+ in annual promotional spend. My focus was architecting and building the Python/Django backend that powered the simulation and optimization engine.
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73
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Himanshu Sharma
PrepLadder | Internal tool for funded startup
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10
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Ajay Bidyarthy- AI Full Stack Developer
I develop and manage databases using Elasticsearch, Neo4j, and Supabase to deliver fast search, powerful data relationships, and scalable backends. I design efficient data models, optimize performance, and ensure secure, reliable data access. I handle everything from setup and integration to deployment and ongoing optimization.
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287
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Mansi Sharma
Led development of the official website for the Embassy of India, Kathmandu using Laravel, ensuring security, scalability, and optimized performance.
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37
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Chirag Ahuja
Trails MCP is an open-source Model Context Protocol (MCP) server that enables AI assistants to search hiking trails, retrieve route details, elevation profiles, and weather data. It integrates multiple outdoor data sources to provide a unified interface for building AI-powered trekking and adventure applications.
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21
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Shivam Barnwal
Tour and Travel Website with booking, payment gateway & VR
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152
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Chaitanya Bajpai
Price Findrr
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8
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1
ANIMESH SINGH
A fully Dockerized real-time IoT ETL pipeline that simulates device telemetry, processes events through MQTT and Kafka, orchestrates workflows with Airflow, and delivers real-time alerts and insights to CRM systems with monitoring via Grafana and Loki.
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71
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Durgesh Jadhav
RMPL HR Management 4+
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4
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Vishakha Sanjay Yadav
What It Is SevaFlow is a civic complaint management system that lets Indian citizens file government complaints through Telegram without downloading any app or creating an account. The complaint gets automatically understood, routed, and tracked using AI. How It Works End to End A citizen sends a plain text message to the Telegram bot describing their problem. That message gets sent to Google Gemini with a carefully designed prompt at temperature 0.1, meaning the AI outputs consistent, deterministic JSON every time. Gemini extracts the issue type, location, responsible department, priority level, and generates a summary, all returning a confidence score between 0 and 1. The routing engine then takes over. It applies priority override rules first, so words like "fire" or "emergency" always trigger urgent regardless of what the AI said. It maps the AI suggestion to a configured department, assigns an SLA deadline based on department and priority, and stores everything in SQLite. The citizen immediately receives a Telegram confirmation with their reference ID like SF1234, department name, priority, and expected response time. The Admin Side Government officials log into a dashboard at the FastAPI server. They can filter and sort complaints, view the full status history of each one showing who changed what and when, update the status with notes like "team dispatched", and trigger a Telegram notification back to the citizen automatically. What Makes It Technically Interesting The AI pipeline has a two layer fallback. If Gemini fails, keyword matching kicks in to identify the department. If that also fails, it routes to General Services with medium priority and confidence marked as 0.0 so admins know it needs manual review. Nothing gets lost. The department configuration is fully data driven. Adding a new government department requires zero code changes, just a new entry in config.py (http://config.py) with keywords, SLA hours, and contact email. The system picks it up on restart. The database tracks two separate tables: complaints with all AI output stored alongside the raw text, and status history with a complete changelog including timestamps and the identity of who made each change. Why It Won Most hackathon civic tech projects build a web form. SevaFlow used Telegram as the interface because that is where citizens already are, made the AI classification reliable enough to actually route correctly, and built the full government side too, not just the submission side. End to end in one system, deployable on a single lightweight server.
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