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Metaviz AI transformed the Fleet app from a concept into a fully functional solution through agile planning and precise execution. We began by gathering detailed requirements and designing a user-friendly interface. Our development focused on real-time data integration, interactive analytics, and seamless map tracking. Rigorous testing and continuous optimization ensured reliability and scalability. This step-by-step approach delivered a robust fleet management tool tailored to meet client needs.
An in-depth consultation identified the client’s fleet administration needs, focusing on key data and visualizing complex telemetry.
Step 1Our design team built an intuitive UI integrating real-time GPS and vehicle sensor data with interactive graphs.
Step 2Using advanced map APIs, Metaviz AI built a live map and track system that improves fleet distribution visibility.
Step 3Rigorous testing ensured smooth app performance under heavy data loads, with fast updates and low latency.
Step 4We ensured secure performance and secure payment integration, launching the app on web, Android, and iOS using scalable APIs and real-time syncing.
Step 5Expertise in staffing and trade platforms
Small business-ready AI integrations
Mobile-first, secure, intuitive UX/UI
Fast, reliable, transparent development flow
Cross-platform AI for growing businesses
Metaviz AI developed the Fleet app using a clear and efficient architecture to guarantee real-time accuracy, scalability, and a user-friendly experience for fleet managers. The app integrates multiple live data points to provide a comprehensive snapshot of vehicle and driver status at any moment.
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To build a seamless, high-performance staffing platform, Metaviz implemented a carefully selected stack of modern technologies. These tools ensure a stable, scalable, and user-friendly experience for both workers and contractors. Miploy’s MVP was developed using cross-platform and cloud-native solutions, supporting real-time operations and future adaptability.
The app’s intuitive design boosted user interaction, encouraging frequent job search activity.
Automated features streamlined hiring, reducing the time spent in the recruitment process.
The platform strengthened relationships between employers and job seekers, building trust and reliability.
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It allows businesses to leverage the latest technology without constant reinvestment. This advance MVP app (Toolbox) offers seamless cross-platform functionality, crucial for:
Since deploying the AI-powered MVP app, trade businesses have seen a 30% increase in lead conversions and a 60% reduction in reporting times. Marketing ROI has improved through more targeted and cost-effective campaigns. Additionally, 70% of customer inquiries are now resolved instantly by AI, significantly enhancing customer support and satisfaction.
30% higher conversions with enhanced lead scoring and follow-ups.
Reporting time reduced by 60%, speeding up decision-making.
Increased efficiency, yielding more targeted, cost-effective ads.
70% of inquiries instantly resolved by AI, improving customer services.
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Without a unified platform, managing jobs and support was inefficient.
Metaviz AI delivered a powerful React-based admin panel to centralize job oversight, user management, and issue resolution in one place.
Job progress tracking and communication were mostly manual.
Metaviz AI simplified this with real-time chat, status updates, and media uploads, enabling smooth communication between customers and artisans.
Expertise in staffing and trade platforms
Small business-ready AI integrations
Mobile-first, secure, intuitive UX/UI
Fast, reliable, transparent development flow
Cross-platform AI for growing businesses
A strong digital foundation underpins Tadwira App’s scalability and performance. Our tech stack was selected for speed, cross-platform flexibility, and ease of maintenance for both mobile users and backend administrators.
To ensure a fast, secure, and scalable product, Metaviz AI used:
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A strong digital foundation underpins Tadwira App’s scalability and performance. Our tech stack was selected for speed, cross-platform flexibility, and ease of maintenance for both mobile users and backend administrators.
To ensure a fast, secure, and scalable product, Metaviz AI used:
Since launching the Tadwira MVP app, the local artisan services market in Morocco has experienced a 30% boost in job response rates and a 50% faster payment reconciliation process. Customer satisfaction has improved significantly, with 85% positive feedback on app usability and service quality.
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Tricktionary chose Metaviz AI for its expertise in intelligent app development, cross-platform delivery, and deep understanding of educational UX. The Metaviz team worked closely with Tricktionary to map out the learning needs of windsurfers and coaches, building a roadmap for MVP execution across five agile phases:
We analyzed how users consume training content and where learning plateaus occur, helping us shape an experience that caters to both beginners and professionals.
Step 1Mapped out learning modules and integrated AI to offer adaptive feedback, dynamic difficulty adjustment, and gamified scoring for progress motivation.
Step 2Crafted wireframes and high-fidelity designs optimized for coaching, video tutorials, feedback prompts, and leaderboards across mobile and web platforms.
Step 3Engineered a cloud-based backend and built the frontend with Flutter. Implemented adaptive learning logic, content streaming, and Firebase analytics.
Step 4We deployed the app with full functionality in English, Spanish, and German, enabling a global launch with region-specific personalization features.
Step 5 Metaviz AI ensured optimal performance by choosing a flexible, scalable tech stack:
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Our tech architecture was selected with long-term evolution in mind. From performance to scalability, every tool was chosen to maximize efficiency across mobile and web layers. This allowed MOE Dashboards to adapt to growing data complexity and user demands without sacrificing speed or reliability. Metaviz AI implemented a secure, fast, and maintainable architecture:
Each technology was chosen for stability, scalability, and ease of integration, ensuring long-term maintainability and performance.
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Yummy Future’s technology stack was built for speed, reliability, and growth. Leveraging Flutter and Firebase, the app ensures real-time performance, secure transactions, and a seamless user experience across devices.
Yummy Future’s goal was to provide a reliable and easy-to-use meal ordering app that delivers speed and personalization. They needed a tech partner who could bring this idea to life fast.
Step 1Metaviz AI recommended Flutter for rapid development across iOS and Android, reducing time-to-market and development costs.
Step 2A robust real-time backend was crucial for handling live order updates, encrypted transactions, and secure user data management.
Step 3Our team focused on a frictionless journey—from exploring the menu to receiving a ready-for-pickup notification—all within a clean, intuitive interface.
Step 4We collaborated closely with Yummy Future to iterate quickly, run performance testing, and launch a fully functional MVP in record time.
Step 5 A robust digital framework powers Yummy Future’s seamless performance and high user satisfaction. The tech stack was chosen for rapid development, real-time responsiveness, and consistent functionality across both mobile platforms.
To ensure a secure, intuitive, and scalable MVP, Metaviz AI implemented:
This stack enabled us to deliver a high-performance app with real-time updates, smooth payments, and cross-platform reliability.
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A robust digital framework powers Medory’s seamless performance and real-time health insights. Metaviz AI selected a tech stack that ensured quick development, secure data management, and scalable performance. To ensure a secure, intuitive, and scalable MVP, Metaviz AI implemented:
This stack enabled us to deliver a high-performance app that provides real-time health updates, secure data management, and an engaging user experience.
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Problem:
BigCommerce merchants previously relied on generic recommendation engines that failed to align with individual shopper preferences, leading to poor engagement and lower conversions.
Solution:
Metaviz AI integrated a behavior-based recommendation system that analyzes user browsing and purchase history. This allowed the app to serve personalized product suggestions, increasing relevance and boosting engagement by 3x.
Problem:
Merchants often experienced overstock or stockouts due to inaccurate sales forecasting, affecting profitability and customer satisfaction.
Solution:
The app introduced a predictive inventory control system powered by AI that tracks sales trends and customer behavior. This resulted in 30% more accurate inventory planning, reducing waste and improving stock availability.
Problem:
Traditional search features lacked smart filtering and voice support, creating friction in the customer journey and limiting product discoverability.
Solution:
Metaviz AI added natural language processing (NLP) and voice search capabilities to the app, enabling users to search more intuitively. This led to a 2x faster product discovery rate, improving user experience and accessibility.
Problem:
Retailers using static pricing missed out on opportunities to stay competitive and optimize margins based on market demand and competitor behavior.
Solution:
A dynamic pricing engine was implemented that updates prices in real-time based on customer demand, trends, and competitor actions. This helped retailers remain agile and contributed to higher conversion rates and profitability.
To ensure optimal performance and flexibility, Metaviz AI selected tools and frameworks that support real-time updates, smart features, and a consistent user experience.
This stack allowed for a robust MVP that performs reliably and enhances every touchpoint of the user journey.
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Snap and Shop’s performance depends on a tightly integrated tech ecosystem. Metaviz AI selected a lightweight yet powerful stack to ensure speed, accuracy, and scalability.
This full-stack approach ensured that the MVP was not just functional but also delightful to use.
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Problem:
Global travel platforms like TripAdvisor often fail to provide tailored, localized content, particularly focusing on hidden gems unique to specific regions like the UAE.
Solution:
MishwarUAE addressed this by offering curated local experiences, showcasing handpicked tours and activities across the Emirates. These experiences highlight cultural insights and hidden gems, ensuring a deeper, more authentic connection for users seeking to explore the UAE beyond traditional tourist spots.
Problem:
Users often struggle to plan personalized itineraries, as existing platforms typically offer generic suggestions that fail to integrate various activities into a cohesive travel plan.
Solution:
MishwarUAE simplified itinerary building with a customizable feature that lets users craft their own travel plans. The app not only suggests personalized experiences but also offers easy-to-use tools to build and adjust itineraries, ensuring a tailored travel experience.
Problem:
Existing platforms often lack personalized recommendations, providing users with generic options that don’t align with their preferences or interests.
Solution:
MishwarUAE leveraged AI-powered recommendations that analyze user behavior and preferences, offering suggestions for activities like desert safaris, cultural tours, and family-friendly experiences. This personalization ensures each user receives a unique and customized travel plan.
Problem:
Travel apps often require constant internet connectivity, which can be problematic for users in areas with poor signal or those traveling without mobile data.
Solution:
MishwarUAE included offline support, allowing users to download maps and itineraries for use when they don’t have access to the internet. This feature ensures that users can continue to navigate and follow their planned activities seamlessly, even in offline mode.
The success of MishwarUAE relied on a powerful tech stack that ensured smooth performance and scalability:
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Problem:
Before Carl, users were often faced with meal plans that didn’t suit their unique dietary needs, preferences, or health goals. Generic suggestions led to frustration, poor adherence, and wasted ingredients.
Solution:
Carl’s AI-driven recommendation engine analyzes individual preferences, dietary restrictions, and goals. By offering personalized meal plans and recipes, Carl increases meal engagement and helps users stay on track with their nutrition, resulting in improved satisfaction and better eating habits.
Problem:
Many users struggled with grocery shopping because they were buying too much or too little. This led to overstocking, food waste, and unnecessary trips to the store.
Solution:
Carl’s smart grocery lists are automatically generated based on the user’s personalized meal plan. The app suggests exactly what users need, helping them save time, reduce waste, and shop more efficiently. This streamlined approach to grocery shopping leads to fewer trips to the store and smarter spending.
Problem:
Tracking pantry inventory manually was time-consuming, and users often forgot about ingredients they had, leading to spoilage and waste.
Solution:
Carl’s pantry management system automatically tracks ingredients in your pantry. The app alerts users when items are running low or nearing expiration, helping them stay organized and reduce food waste. This feature ensures users make the most of what they already have.
Problem:
Meal planning could be overwhelming, especially when trying to come up with healthy, nutritious meals that fit individual preferences and goals.
Solution:
With AI-generated recipes and tailored suggestions, Carl takes the guesswork out of meal planning. The app not only suggests meals based on dietary preferences but also ensures that meal preparation time is minimized. This efficiency empowers users to stick to a healthy eating plan without stress.
Metaviz AI carefully selected the tools and technologies to ensure Carl offers a seamless experience:
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Problem:
Before Audio Book, many users struggled to find time to read full books due to busy schedules. This led to missed opportunities for personal growth and learning.
Solution:
Audio Book provides book summaries in audio format, allowing users to consume knowledge while on the go. Whether commuting, exercising, or relaxing, users can absorb key insights from books in less time.
Problem:
Users often had difficulty finding specific book summaries or relevant content among a sea of available options.
Solution:
Audio Book offers a powerful search, filter, and explore feature that makes it easy for users to find the books or topics they are interested in. This streamlines the discovery process.
Problem:
Many users prefer consuming content in audio format but lacked a platform that provided easy access to audio book summaries.
Solution:
Audio Book delivers all summaries in audio format, making it convenient for users to listen while multitasking. This feature enhances accessibility and provides a better alternative for users who prefer listening over reading.
Problem:
Users were limited to the books available in their personal library or through text-based summaries, reducing variety and flexibility.
Solution:
Audio Book offers a wide library of book summaries across various genres. The app ensures users have access to a broad selection, allowing them to explore new topics and expand their knowledge without limitations.
Audio Book was built using modern technologies to ensure a seamless user experience and scalability:
This tech stack ensures that Audio Book is fast, reliable, and adaptable to growing user needs.
Audio Book has transformed the way people learn, offering a convenient and efficient solution for users to consume knowledge:
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Problem:
Before Taxi Landskrona City, users often faced delays in getting a ride, especially during peak hours or in less busy areas. This led to frustration and longer waiting times.
Solution:
Taxi Landskrona City allows users to book rides instantly or schedule them in advance. The app offers flexibility, ensuring users can get a ride when they need it, cutting down on waiting times and improving the overall experience.
Problem:
Many traditional taxi services had limited payment methods, which created inconvenience for users who preferred to pay using digital methods like Apple Pay or Google Pay.
Solution:
Taxi Landskrona City supports multiple payment options, including cash, card, Apple Pay, and Google Pay. This flexibility ensures users can pay in the way that is most convenient and secure for them.
Problem:
Users often had no way to track their ride or see driver details before the trip, which created uncertainty and insecurity.
Solution:
Taxi Landskrona City offers real-time ride tracking, allowing users to see the driver’s location and estimated arrival time. This transparency builds trust and provides a safer and more reliable ride experience.
Problem:
Users had difficulty keeping track of their ride history and payment details, which created frustration when managing finances or re-booking rides.
Solution:
Taxi Landskrona City gives users access to trip history and wallet details directly in the app. This feature makes it easy for users to view past trips, manage payments, and stay organized.
Taxi Landskrona City was built using modern technologies to ensure smooth operation and scalability:
This tech stack ensures that Taxi Landskrona City operates efficiently and can scale to meet growing demand.
With its easy-to-use features and reliable service, users are coming back for more rides.
Real-time tracking and faster booking have helped fulfill ride requests more quickly.
The app’s variety of payment options and user-focused features have increased overall revenue.
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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
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.
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.
Users often didn’t know where to find trusted help.
Solution: Integrated Yelp API to display top-rated nearby arborists based on user location.
Non-technical users needed help navigating options.
Solution: Deployed a LangChain + Gemini-powered chatbot with memory, real-time tool use, and personalized follow-ups.
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
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
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
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
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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.
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.
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.
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.
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.
Any Client (React.js, Postman, etc.): Sends requests and renders article output.
Step 1FastAPI: Handles endpoints for article generation, extraction, and health checks.
LangGraph: Orchestrates the multi-step workflow (input → research → generation → validation).
Google Gemini: Generates draft content.
OpenAI GPT-4o: Validates content for factual accuracy and formatting compliance.
Perplexity: Literature search fallback
Firecrawl: Web scraping
RapidAPI: YouTube transcription
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.
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.
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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.
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.
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.
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.
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.
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
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
Classifies user queries and assigns LLM + Agent
Ranks outputs using a confidence-based scoring system
Supports tools like search, API calls, and reasoning agents
Password hashing & secure token storage
OTP email verification with nodemailer
CORS setup for frontend/backend sync
Role-based access using JWT tokens
LiveIn Helper is a user-friendly web platform designed to facilitate home-sharing for seniors who require in-home care. It connects seniors with caregivers seeking affordable housing and care opportunities. The platform allows seniors to create personalized profiles and find caregivers who match their specific needs. With a responsive interface, seniors can browse caregiver options, communicate securely, and manage their home-sharing arrangements all in one place.
Built with HTML, CSS, Bootstrap, and backed by PHP and MySQL, LiveIn Helper focuses on providing a secure and simple solution for both seniors and caregivers. The intuitive design ensures accessibility for seniors, even those with limited technical experience, while offering reliable backend technology for secure data management.
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.
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.
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.
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.
HTML, CSS, Bootstrap: Provides a responsive, easy-to-navigate design for seniors.
JavaScript/jQuery: Adds interactivity and dynamic content to the platform.
PHP: Handles all backend operations, including profile management and secure messaging.
MySQL: Stores and manages user data, including profiles and communication.
Encrypted Messaging: Ensures privacy and security for users through encrypted communication.
Data Encryption: Protects sensitive user information using MySQL encryption.
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