Built a caregiving marketplace from the ground up — a large two-sided platform that connects people who need help with vetted professionals looking for work.
The project included the full development cycle: user journey research, architecture design, UI/UX design, frontend and backend development, implementation of a complex filtering system, personal dashboards, server infrastructure, and fault-tolerance mechanisms.
By using LLM tools in development, we were able to move faster through the design, implementation, and testing stages, reduce project costs compared with proposals from traditional teams, and still launch a stable, secure platform ready to scale.
Challenge
The client wanted to build a full-featured caregiving marketplace that would bring together two user groups.
On one side — people who need help caring for an elderly, mobility-limited, or temporarily dependent family member.
On the other side — caregivers and care professionals looking for jobs, ongoing work, or shifts on a set schedule.
The platform needed to:
- help clients find the right caregiver;
- provide detailed professional profiles;
- support advanced search across a large number of parameters;
- take schedule, experience, skills, location, and work conditions into account;
- give caregivers the ability to create and edit their profiles;
- provide convenient interaction between both sides;
- perform reliably as the user base grows;
- protect personal data;
- offer a modern, intuitive interface;
- support future business growth and scaling.
The main task was not just to create a website with a profile directory.
We needed to build a complete digital platform with two user types, different business scenarios, complex matching logic, high performance, and a reliable server infrastructure.
Problem
Finding a caregiver is a complex and sensitive process.
The user is not choosing a typical service provider, but a person who will care for a relative, stay with them at home, and often handle medical or household tasks.
When making the match, it is important to consider many factors:
- work experience;
- qualifications;
- caregiving skills;
- ability to work with specific conditions;
- medical training;
- employment format;
- schedule;
- service rates;
- location;
- live-in availability;
- willingness to work night shifts;
- physical limitations;
- language skills;
- additional family requirements.
A simple directory with a few filters would not have made it possible to match the right professional accurately enough.
Caregivers also needed a convenient tool. The professional had to be able to describe their experience in detail, specify work conditions, update availability, and receive relevant offers.
Another challenge was the system’s scale. A large number of profiles, photos, filters, search queries, and user actions creates a heavy load on the backend and database.
Without a properly designed architecture, the platform could have loaded slowly, become unstable as traffic grew, and become increasingly difficult to develop.
Solution
We designed and built the caregiving marketplace as a full web product, not just a collection of separate pages.
The system was built around two main user roles:
- the client, who is looking for a care professional;
- the caregiver, who creates a profile and looks for work.
For each role, we developed separate workflows for registration, profile completion, search, data management, and platform interaction.
We built the entire user interface, frontend, backend, database, and server infrastructure from scratch.
Special attention was paid to search speed and filter performance. The data structure and queries were designed so users would get relevant results quickly, even with a large number of profiles.
The platform is deployed on a multi-server infrastructure with redundancy, monitoring, and recovery mechanisms. This reduces downtime risk and makes it possible to add resources as the project grows.
LLM tools were used at different stages of development: requirements analysis, module design, generation of standard components, code review, test preparation, and identification of potential bugs.
This reduced routine work, made it faster to validate hypotheses, and allowed the team to spend more time on architecture, security, and core business logic.
What We Built
Two-Sided Marketplace
The platform brings together two independent user groups with different needs.
Clients can search for caregivers, review profiles, compare conditions, and choose professionals for a specific situation.
Caregivers can create professional profiles, list experience and skills, define their preferred schedule, and specify cooperation terms.
Each side has its own dashboard and its own logic for using the service.
This approach makes it possible to grow the marketplace in two directions at once: attract people who need care while also expanding the pool of professionals.
Registration and Dashboards
We developed registration and login flows for different user types.
After registering, the client gets access to search tools and data management features.
A caregiver can complete an extended profile, upload a photo, specify experience, skills, availability, service rates, and other parameters.
In the dashboard, the user can:
- edit the profile;
- change contact information;
- manage settings;
- update availability information;
- view account-related data;
- use the platform’s core features.
Role-based access control prevents users from using features not intended for their specific user type.
Detailed Caregiver Profiles
The professional profile was designed so the client could make a decision without having to gather information from additional sources for a long time.
The profile can include:
- name and photo;
- age;
- city and service area;
- experience;
- education;
- medical training;
- list of skills;
- experience working with specific medical conditions;
- live-in availability;
- willingness to work night shifts;
- employment type;
- preferred schedule;
- service rate;
- language skills;
- additional information about the specialist.
The form structure is both user-friendly and suitable for automated search.
Each parameter is stored in a structured format and can be used to filter and rank results.
A complex filter system
One of the marketplace's core functions became searching for caregivers using a wide range of criteria.
Users can narrow the selection step by step and find a specialist who fits a specific situation.
The filtering can take into account:
- location;
- service rate;
- experience;
- work schedule;
- employment format;
- presence of medical skills;
- live-in availability;
- night work;
- experience caring for people with specific medical conditions;
- language proficiency;
- additional requirements.
The filters work together and generate complex database queries.
We optimized the data storage structure, indexes, and server-side request processing so results would be generated quickly even when a large number of conditions are combined.
Search and relevant results
Users should not just get a list of all matching profiles, but see the most relevant specialists.
That is why, when generating results, the system considers not only exact filter matches but also the full set of profile parameters.
Profiles can be ranked based on:
- match with requirements;
- profile completeness;
- data freshness;
- specialist availability;
- geographic proximity;
- other platform business parameters.
The architecture makes it possible to further develop the matching algorithm and add personalized recommendations.
Design and user journeys
We fully developed the platform design.
The interface was built with the understanding that people of different ages and digital skill levels may use the service.
The main actions were made clear and sequential:
- registration;
- creating a profile;
- entering parameters;
- setting up filters;
- viewing results;
- reviewing a profile;
- managing the personal account.
Special attention was given to readability, navigation, forms, and the mobile version.
Users can work with the platform from both a computer and a smartphone without losing key functionality.
Platform frontend
The frontend was developed as a separate, fully featured application with a responsive interface.
During development, we focused on:
- initial load speed;
- dynamic data updates;
- proper operation of complex forms;
- preserving filter state;
- mobile adaptation;
- error handling;
- interface accessibility;
- ease of future expansion.
The interface components were standardized, which speeds up the addition of new pages and features.
Backend and business logic
The backend is responsible for registration, authentication, profile management, filtering, data storage, and enforcing business rules.
The server side handles:
- user accounts;
- roles and access permissions;
- specialist profiles;
- search parameters;
- file and photo uploads;
- catalog requests;
- user settings;
- internal platform operations.
Business logic is separated from the user interface and infrastructure components.
This simplifies testing and makes it possible to develop the frontend, backend, and mobile apps independently if they are added in the future.
Multi-server infrastructure
The project was designed for future growth, so the infrastructure was not limited to a single server.
The system is distributed across several server-side components. Different parts handle web requests, application logic, data storage, and supporting processes.
This approach makes it possible to:
- distribute load;
- increase computing resources;
- serve more users;
- update individual components with minimal downtime;
- reduce dependence on a single server;
- restore service faster after failures.
The infrastructure is ready for horizontal scaling as the audience and number of profiles grow.
High performance
For a marketplace with a large number of filters, performance is part of the core functionality.
We optimized:
- database structure;
- queries;
- indexes;
- server-side processing;
- data transfer between frontend and backend;
- image loading;
- caching;
- background operations.
Heavy actions do not block the user interface, and frequently requested data can be processed without repeating resource-intensive operations.
As a result, pages, profiles, and search results load quickly, and the system remains stable as traffic grows.
Fault tolerance
The platform includes mechanisms that reduce the risk of a complete service outage due to a failure in an individual component.
Implemented:
- service separation;
- automatic process restart;
- health monitoring;
- backup;
- error logging;
- availability monitoring;
- isolation of critical components;
- data recovery capability.
This is especially important for a marketplace where platform downtime affects both clients and specialists at the same time.
Security
The platform handles personal data, so security was considered at the architecture, backend, and infrastructure levels.
We implemented:
- secure authentication;
- role and permission separation;
- input validation;
- server-side method protection;
- secure file handling;
- restricted access to personal information;
- protection against common web vulnerabilities;
- logging of important operations;
- secure storage of configuration and keys.
A user cannot access data or actions that are not allowed by their role.
Testing and quality control
A large amount of logic requires systematic testing.
We checked:
- registration and authentication;
- the work of different roles;
- creating and editing profiles;
- filtering;
- search;
- access rights;
- data loading;
- handling invalid requests;
- system behavior in error cases;
- performance on mobile devices;
- stability of key user flows.
LLM tools helped create test scenarios faster, analyze code, and identify potential edge cases.
At the same time, final decisions went through manual engineering review. AI did not replace architectural oversight, code review, or testing; it accelerated them.
Using LLMs in development
One of the project’s advantages was the systematic use of LLM tools in the development process.
They were used for:
- breaking down requirements;
- refining user scenarios;
- preparing draft components;
- generating standard code;
- reviewing repetitive logic;
- creating tests;
- analyzing errors;
- preparing documentation;
- refactoring individual modules.
As a result, the team spent less time on routine work and more time on the complex parts of the product: architecture, performance, security, and business logic.
This made it possible to deliver the platform faster and at a lower budget than teams working primarily in a traditional development model.
At the same time, code was not accepted without review. Every module went through validation, testing, and integration with the rest of the system.
Technical foundation
The project is built on a modern client-server architecture.
The technical solution includes:
- responsive web application;
- separate backend;
- relational database;
- indexing system for fast search;
- caching;
- file storage;
- background processes;
- containerization;
- multi-server deployment;
- load balancing;
- backup and recovery;
- monitoring and logging;
- CI/CD processes;
- LLM tools for development and quality control.
The architecture is divided into independent layers:
- user interface;
- API;
- business logic;
- user management;
- specialist directory;
- filtering and search;
- file handling;
- database;
- cache;
- background tasks;
- infrastructure and monitoring.
This separation makes it possible to update individual components without a complete rebuild of the system.
In the future, the platform can be expanded with a mobile app, online payments, shift booking, an internal chat, reviews, document verification, ratings, and new monetization models.
Result
The client received a ready-made caregiver marketplace that meets the needs of both sides of the platform.
People who need a care specialist can enter detailed requirements and quickly find relevant profiles.
Caregivers get a separate tool to present their skills, experience, and working conditions.
The platform runs quickly, supports complex filtering, and is ready for audience growth. Multi-server architecture, redundancy, and monitoring help reduce downtime risks.
The client received not a prototype or a template website, but a full digital product with its own logic, design, and infrastructure.
Using LLMs in development made it possible to shorten timelines and reduce budget without giving up engineering best practices. Automation was used to speed up routine stages, while architecture, security, and critical logic were controlled by developers.
As a result, the project was delivered faster and more cost-effectively than traditional team proposals, while preserving quality, stability, and the ability to scale further.
What the client received
- A full-featured two-sided marketplace.
- Separate user flows for clients and caregivers.
- Modern responsive design.
- Detailed specialist profiles.
- A sophisticated filter system.
- Fast search across a large number of parameters.
- Personal user accounts.
- Fully developed frontend and backend.
- Optimized database.
- Multi-server infrastructure.
- Fault tolerance and recovery mechanisms.
- Personal data protection and access control.
- Tested user flows.
- An architecture designed for future growth.
- Reduced development time and cost through the use of LLMs.
The project was developed as a scalable commercial platform.
The clients got a convenient way to find care specialists, caregivers got a new channel for finding work, and the service owner got a full-fledged technology product that can be developed, monetized, and scaled.