HomeMade - Chef MarketPlace

My Role

I was responsible for designing, developing, and deploying the entire Chef Hiring Marketplace.

This included creating the user interface, building the back-end, implementing features like real-time chat, search, filtering, and geolocation, and ensuring a smooth and efficient user experience from start to finish.

What is HomeMade

HomeMade Marketplace is a web-based hiring platform designed to improve how individuals and businesses discover and engage professional chefs for various culinary needs.

It bridges the gap between culinary experts and potential clients through a seamless digital experience, enhancing accessibility, convenience, and quality in the culinary services industry.

1. The Problem

The culinary industry has a growing demand for personalized, professional cooking services. However, finding and hiring skilled chefs remains an inefficient, fragmented, and often frustrating process.

a. User Challenges: Difficulty in finding chefs with specific skill sets or dishes they specialize in. Lack of location-based recommendations for chefs or dishes. Inability to assess chefs’ qualifications and work history before hiring.

b. Business Challenges: Need for a scalable and responsive platform for real-time interactions. Integration of robust filtering and proximity-based search functionality.

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Onboarding Process

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    Profiles and Details

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      Search and Discovery

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        Real-Time Chat

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          Client Persona

          Sarah, a working mother looking for chefs nearby who can prepare healthy meals for her family.

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          Chef Persona

          Chef Alex, a freelance chef looking to expand his client base and showcase his culinary expertise.

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          Scenario 1: Hiring a Chef
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          1. Sign-Up

          User signs up and sets their location.

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          2. Onboarding

          Users provide basic information to complete their profile, ensuring they can fully utilize the platform's features.

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          3. Search and Discovery

          Searches for chefs or dishes based on filters.

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          4. Profiles Visit

          Visits a chef’s profile to view certifications and reviews.

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          5. Chat

          Initiates a chat to discuss availability.

          Scenario 2: Chef Onboarding
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          1. Registration

          Chef signs up and uploads certifications and employment history.

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          2. Onboarding

          Users provide basic information to complete their profile, ensuring they can fully utilize the platform's features.

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          3. Profile Completion

          Adds dishes, specialties, and pricing.

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          4. Engagement

          Interacts with users and manages bookings.

          Frontend

          Next.js for the web platform ensuring a responsive and intuitive user interface.

          Backend

          Node.js with Express.js for a scalable and efficient server-side architecture.

          Database

          MongoDB for its flexibility in managing dynamic chef profiles, dishes, and user data.

          Real-Time Chat

          Socket.IO was implemented to enable WebSocket-based instant messaging between users and chefs.

          a. Onboarding Flow

          1. Designed dynamic onboarding forms using React Hook Form for validation and reactivity.
          2. Integrated geolocation features via browser APIs and GPS

          b. Search and Filter Functionality

          1. Developed robust backend queries in MongoDB, enabling users to filter chefs by location, name, or cuisine type efficiently.
          2. Optimized location-based searches by indexing geospatial data, ensuring quick and accurate proximity calculations.

          c. Chef Profiles

          1. Designed a schema to store detailed chef profiles, including certifications, employment history, and dish portfolios.
          2. Integrated Firebase for managing image uploads, ensuring high-quality visuals of dishes and chefs’ work.

          d. Real-Time Chat

          1. Implemented real-time messaging with Socket.IO, allowing users and chefs to communicate seamlessly.
          To ensure a fast and responsive platform:
          1. Lazy Loading: Images and assets were lazy-loaded to reduce initial page load times.
          2. Debounced Search Inputs: Reduced the load on backend servers by debouncing search inputs, minimizing unnecessary API calls during user input.
          3. API Optimization: Streamlined API calls for distance calculations by leveraging optimized queries and caching mechanisms where applicable.
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