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Next-Gen Web Apps Powered by AI

Explore how AI-driven web applications are transforming industries by enhancing personalization, automating workflows, and delivering smarter, more efficient online solutions tailored to user needs.

AI Powered Web Apps

Tree Care AI Web Application Case Study

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AI Powered Web Apps

MeddAI: Medical Content Creator

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AI Powered Mobile Apps

GoAI Chat Web Application Case Study

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Next-Gen Web Apps Powered by AI

Explore how AI-driven web applications are transforming industries by enhancing personalization, automating workflows, and delivering smarter, more efficient online solutions tailored to user needs.

AI Powered Web Apps

Tree Care AI Web Application Case Study

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View Full

AI Powered Web Apps

MeddAI: Medical Content Creator

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View Full

AI Powered Mobile Apps

GoAI Chat Web Application Case Study

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Tree Care AI Web Application Case Study

Project Overview

TreeCare is an AI-powered web application built to modernize tree care services. It allows users to upload plant images for species identification, check plant health, estimate tree trimming/removal costs, and connect with nearby arborists, all through a responsive web interface.

The app features a React frontend and a backend powered by FastAPI, Node.js, and MongoDB. A smart AI chatbot, powered by LangChain and Google Generative AI (Gemini), guides users through every step, from identifying plants to estimating service prices.

Relevant keywords: AI tree care app, plant identification tool, arborist finder, tree removal cost calculator, chatbot for landscaping services

Challenge & Solution

Image-Based Plant Identification

Users needed quick, accurate diagnostics from photo uploads.
Solution: Integrated Plant.id API via FastAPI to deliver species, health status, and disease suggestions based on uploaded images.

Dynamic Tree Service Cost Estimation

Accurate pricing had to consider multiple variables.
Solution: Built cost calculators using FastAPI and integrated data from RentCast, Google Maps, and AccuWeather to factor in location, urgency, and weather conditions.

Finding Local Arborists

Users often didn’t know where to find trusted help.
Solution: Integrated Yelp API to display top-rated nearby arborists based on user location.

User Guidance and Interaction

Non-technical users needed help navigating options.
Solution: Deployed a LangChain + Gemini-powered chatbot with memory, real-time tool use, and personalized follow-ups.

Tech Stack Implementation

Frontend

React + Tailwind CSS: For fast, responsive UI
Zustand & React Hook Form: For state and form handling
Zod + React Query: For input validation and async data
Axios + Sonner: For API communication and user feedback

Step 1

Backend

FastAPI: Main API logic (chat, pricing, image upload)
Node.js/Express.js: Additional endpoints and MongoDB integration
MongoDB: Stores user data, chats, and service requests

Step 2

AI & APIs

LangChain + LangGraph + Gemini AI: Smart, conversational chatbot
Plant.id API: Image-based plant diagnostics
Yelp API: Arborist search
AccuWeather + RentCast + Google Maps APIs: Cost estimation data points

Step 3

Security

Environment variables: For API key safety
Input and file validation: Using Zod and Pydantic
CORS setup and error handling: Ensures secure and user-friendly experience

Step 4

Results & Value

1

Reduced service estimation time by 80%

2

Delivered image-based plant diagnosis in under 5 seconds

3

Increased arborist bookings by integrating location-based suggestions

4

Delivered a seamless AI-guided experience through a chatbot

MeddAI: Medical Content Creator

Project Overview

MeddAI is an AI-powered web application built to generate trustworthy, citation-ready medical content for healthcare professionals, students, and the public. It uses a FastAPI backend with Python, orchestrates multiple AI agents using LangGraph, and integrates Google Gemini and OpenAI GPT-4o for content generation and validation. Perplexity AI is used for research when no external references are provided.

The platform accepts inputs like URLs, DOCX/PDF files, and YouTube videos. It performs real-time source extraction, automated medical accuracy checks, and delivers AMA-style formatted articles. A REST API interface allows seamless content requests by both technical and non-technical users.

Challenge & Solution

Validating Medical Accuracy

Problem: Risk of generating inaccurate or non-compliant content.
Solution: Implemented GPT-4o for post-generation validation, checking factual accuracy, AMA citation format, and readability. Invalid drafts are automatically flagged and returned with issue logs.

Ingesting Multiple Content Types

Problem: Required support for diverse sources like PDFs, DOCX, and videos.
Solution: Integrated Firecrawl (web), PyPDF2/python-docx (documents), and RapidAPI (YouTube) for seamless ingestion and parsing.

Dynamic Audience Adaptation

Problem: One-size-fits-all tone fails across user groups.
Solution: Language generation dynamically adjusts tone and jargon to fit doctors, students, patients, or general public based on the request context.

Creating Citation-Enforced Content

Problem: Generated content lacked scholarly reference formatting.
Solution: Google Gemini is instructed with strict prompting to embed in-text citations and meet academic formatting standards.

Tech Stack Implementation

Frontend

Any Client (React.js, Postman, etc.): Sends requests and renders article output.

Step 1

Backend

FastAPI: Handles endpoints for article generation, extraction, and health checks.
LangGraph: Orchestrates the multi-step workflow (input → research → generation → validation).

Step 2

AI Models

Google Gemini: Generates draft content.
OpenAI GPT-4o: Validates content for factual accuracy and formatting compliance.

Step 3

External APIs

Perplexity: Literature search fallback
Firecrawl: Web scraping
RapidAPI: YouTube transcription

Step 4

AI Recommendation Engine

Automatically assigns the appropriate workflow depending on input type (URL, PDF, DOCX, video).
Google Gemini handles generation with word count accuracy (±10%).
GPT-4o performs error detection and citation validation.
Perplexity is triggered when no external source is available.

Step 4

Security

Asynchronous validation and rate-limiting for abuse protection.
API keys and endpoints are protected with secure access layers.
User data is handled with HIPAA-conscious security best practices.

Step 4

Results & Value

1

Evidence-Based Articles: Drafts are verified and citation-compliant using AMA formatting.

2

Source-Aware Ingestion: Pulls from multiple file types and sources automatically.

3

Flexible for All Users: Adapts tone and technical depth for all audiences.

4

API Integration: Ready for integration into EMR systems, LMS platforms, or third-party content hubs.

GoAI Chat Web Application Case Study

Project Scope

GoAI is an AI-powered chat application designed to deliver dynamic, personalized conversational experiences. It intelligently matches user queries with the most suitable language model (LLM) and agent combination in real time. The app integrates an LLM recommendation engine, model-agent orchestration, and memory-based conversations—all within a modern UI built using React.js.
By leveraging technologies like LangChain, OpenAI, Gemini, and FastAPI, GoAI ensures users receive context-aware responses with accurate information, whether for education, business, or support. It features secure JWT authentication, dynamic chat memory, and role-based data access.

Challenge & Solution

Matching Queries to the Best LLM

Problem: Users needed accurate results, but no single LLM worked best for all queries.
Solution: GoAI uses a dynamic model-agent recommendation system that selects the optimal LLM and agent combo based on query content and confidence scoring.

Secure User Management

Problem: The app needed secure access control and user data management.
Solution: Implemented JWT-based authentication, email/password login, and OTP verification, with separate flows for new user registration and login.

Maintaining Conversation Context

Problem: Conversations were fragmented without memory.
Solution: Integrated LangGraph and MemorySaver to persist user history, enabling context-aware follow-ups and dynamic multi-turn responses.

Seamless UI for Multiple Devices

Problem: Users accessed the app on various screen sizes.
Solution: Created a responsive frontend with React.js and Tailwind CSS, including dark mode, toggle layouts, and chat UI enhancements.

Tech Stack Implementation

Frontend

React.js: Core UI framework
Tailwind CSS: Utility-first styling
React Router & Axios: Navigation and API requests
JWT Auth + React Hook Form: Form validation and access control

Step 1

Backend

FastAPI + Node.js/Express: API logic and model integration
MongoDB: Stores user data, conversations, and access logs
LangChain + LangGraph: Dynamic chatbot agent orchestration
OpenAI, Gemini APIs: LLM outputs and reasoning

Step 2

AI Recommendation Engine

Classifies user queries and assigns LLM + Agent
Ranks outputs using a confidence-based scoring system
Supports tools like search, API calls, and reasoning agents

Step 3

Security

Password hashing & secure token storage
OTP email verification with nodemailer
CORS setup for frontend/backend sync
Role-based access using JWT tokens

Step 4

Results & Value

1

Dynamic AI Pairing

Auto-selects the best LLM + agent per use case
2

Custom Chat Experience

Maintains memory and provides smart follow-ups
3

Scalable Design

Easily integrates new models, tools, or agent workflows
4

Clean UI

Accessible across all devices, including dark mode and responsive scaling