InstantService

Inspiration

InstantService was inspired by a common problem: finding a reliable service technician is often slow, stressful, and uncertain. When someone needs a plumber, electrician, mechanic, maintenance worker, or other skilled service provider, they usually have to search across multiple websites, compare reviews, call different people, wait for replies, and hope the technician is trustworthy.

We wanted to build a platform that makes this process faster and safer. Instead of making users search manually, InstantService lets clients describe what they need and helps connect them with verified local technicians based on service type, location, availability, and trust signals.

What it does

InstantService is an AI-powered service booking platform that connects clients with verified local service technicians.

A user can submit a service request, such as:

“My kitchen sink is leaking and I need someone today.”

The platform can analyze the request, identify the service category and urgency, and match the client with relevant technicians nearby. Technician profiles can include certifications, licenses, service range, rates, reviews, availability, and previous work.

The goal is to create a faster and more secure experience for both sides of the marketplace:

• Clients get quicker access to trusted service providers.
• Technicians get a better way to receive qualified job requests.

How we built it

We built InstantService with a backend-first architecture designed to support a full service-booking flow.

The backend uses FastAPI to handle API routes, health checks, contractor data, and future booking logic. We structured the backend so different features can be organized into separate routes, such as contractor management, request analysis, dispatch, bookings, and voice support.

We used Snowflake as the structured data layer for storing and managing important marketplace data, including users, contractors, bookings, reviews, locations, and availability. This helps make the platform scalable and data-driven.

We designed the AI layer around Gemini API, which can transform natural-language service requests into structured information. For example, it can identify whether a request is related to plumbing, automotive repair, electrical work, or another service category.

We also included ElevenLabs in the project concept to support voice-based interaction, such as spoken confirmations, voice request creation, and accessibility-friendly communication.

The project flow is:

Client Request
    ↓
FastAPI Backend
    ↓
AI Request Analysis
    ↓
Snowflake Data Layer
    ↓
Technician Matching
    ↓
Booking Confirmation

Built With

• digitalocean
• elevenlabs
• geminiapi
• javascript
• python
• snowflake