PartTime Usta
Description
PartTime Usta is a mobile service marketplace designed to connect users with qualified professionals for repair, renovation, maintenance, and other household service needs.
Users can create a service request by describing the problem through text, images, or video. The platform then evaluates the request and connects it with an appropriate professional, reducing the time and uncertainty involved in finding reliable support.
The product also creates a structured work channel for professionals by helping them reach potential customers, receive relevant job opportunities, and build a measurable service history through customer feedback. An AI-supported assignment model is used to improve the speed and relevance of customer–professional matching.
Creative Strategy
The creative strategy for PartTime Usta was built around trust, speed, and accessibility. Household service problems are often urgent and difficult to explain, so the experience was designed to let users communicate their needs quickly without navigating complex forms.
Text, image, and video-based request creation were positioned as the core of the experience. This approach allows users to describe real-world problems naturally while giving professionals more context before accepting a job.
The visual and interaction system was structured to make the service journey feel transparent from the first request to final evaluation. Clear status communication, simplified actions, and direct service flows help reduce uncertainty for both customers and professionals.
The platform was positioned not only as a listing service, but as an operational bridge that manages demand, matching, service delivery, and quality feedback within one connected mobile experience.
Technical Execution
- (01)
A multi-format service request system was developed to support text, image, and video input within a single workflow. This structure allows users to provide detailed context while keeping the request creation process fast and accessible.
- (02)
An AI-supported assignment flow was integrated to evaluate service requests and improve the relevance of professional matching. The system was structured to reduce manual search effort and accelerate the connection between customers and available professionals.
- (03)
Separate customer and professional workflows were organized within the same product ecosystem. Request creation, job assignment, service progress, completion, and evaluation were connected through consistent data and status models.
- (04)
A service evaluation structure was implemented to measure work quality after completion. Customer feedback contributes to professional performance records and supports a more reliable service network over time.
- (05)
The application architecture was designed around modular service categories, scalable request management, and maintainable operational flows. This approach supports the addition of new service types, matching rules, and professional management capabilities as the platform expands.