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Local Tradesman Search

Azati built a platform to connect users with local tradesmen, including web and mobile apps, smart search, and integrated payments with digital and international options.

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All Technologies Used

Python
Python
TensorFlow
TensorFlow
Rails
Rails
JavaScript
JavaScript
Ruby
Ruby
MongoDB
MongoDB
Android
Android
iOS
iOS
NLP
NLP

Motivation

The customer aimed to launch an online services marketplace where local tradesmen could offer services, build trust through a reputation system, and receive payments easily. A key goal was implementing a fast and intelligent search engine capable of understanding complex human-like queries via natural language processing.

Main Challenges

Challenge 1
High Lead Generation Costs

The customer needed a cost-effective alternative to SEO and PPC for attracting clients to the platform. We offered an online marketplace where tradesmen could gain visibility through organic search and reputation-based ranking instead of paid promotion.

Challenge 2
Low Search Relevance and User Frustration

The existing search solutions couldn’t handle natural, complex queries, leading to irrelevant results. We decided to built a machine learning-powered search engine with NLP capabilities to understand user intent and deliver accurate matches.

Challenge 3
Lack of Trust Between Users and gProviders

The customer needed a mechanism for building trust between service providers and clients. Our team implemented a transparent reputation system based on completed jobs and real user reviews, removing the need for tradesmen to pay for visibility.

Key Features

  • NLP-Based Smart Search: Users can search using natural, conversational language thanks to the machine learning-powered engine.
  • Customizable Profile Pages: Tradesmen showcase services, portfolios, and client feedback via personalized pages.
  • Reputation System: User ratings and completed job feedback build trust and determine visibility, not paid promotions.
  • Loyalty and Referral Programs: Gamified incentives such as referral bonuses and early registration rewards support user growth.
  • Multi-Platform Access: The solution includes synchronized web, iOS, and Android applications.
  • Billing System Integration: Supports multiple payment gateways, including digital currency.

Our Approach

Rapid MVP Development with Ruby on Rails
We chose Ruby on Rails to quickly develop and deliver a Minimal Viable Product, using the framework's modularity to expand functionality over time.
Machine Learning-Powered Search Engine
Data extracted from websites was unstructured. We used a NoSQL database to store this data in a structured format, which allowed recruiters to search and evaluate candidates effectively.
Full-Cycle Development
Azati managed the entire development cycle, including project management, frontend and backend development, mobile apps, and infrastructure setup.
Community-Driven Reputation System
We enabled tradesmen to maintain profile pages with reviews and job ratings, fostering trust through completed work rather than paid promotion.
Loyalty and Referral Program
We implemented gamified mechanics like referral bonuses and early registration points to encourage adoption and reduce subscription costs for users.

Project Impact

Improved Search Accuracy and Speed: The machine learning engine increased local search efficiency by 272% compared to traditional keyword-based algorithms.

Faster Time-to-Market: Ruby on Rails enabled fast MVP delivery and easy future enhancements.

Higher User Engagement: Reputation and community-based features increased user trust and platform retention.

Business Diversification Success: The client successfully entered a new industry and launched a competitive online services marketplace.

Cost-Efficient Lead Generation for Tradesmen: The platform provided tradesmen with an affordable and scalable alternative to PPC advertising.

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