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Inventory Search Engine for Auto Parts Retailer

Azati designed and developed an intelligent search engine for inventory search enhancing traditional search algorithms. It analyzes the user input and looks for a specific entry. If the algorithm can’t find the requested item in the inventory, it explores the characteristics of the object and returns the list of similar products.

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All Technologies Used

Python
Python
Flask
Flask
React
React
Tornado
Tornado
Memcached
Memcached

Motivation

A Brazilian online retailer of automotive parts and accessories faced slow and inaccurate search results in their massive catalog, containing 500,000 parts and 100,000 auto-related products. Azati's solution aimed to optimize the search process, moving away from the existing ineffective solution to a more efficient one capable of handling large volumes of data, improving speed and accuracy of product searches.

Main Challenges

Challenge 1
Handling multiple catalog formats

The client’s catalog data was spread across various formats including XLSX, TXT, CSV, MySQL databases, and APIs. Azati developed universal connectors for each format to standardize and streamline data extraction into one well-structured format.

Challenge 2
Building an intelligent search engine

The search engine had to quickly analyze user queries, match them against product attributes, and return relevant results. Azati implemented smart tagging of auto parts with unique attributes to ensure accurate search and fast retrieval.

Challenge 3
Real-time data lookup and UI updates

The system had to refresh search results in real-time as users entered more search terms. Azati implemented an in-memory search approach, bypassing database bottlenecks to provide faster query results and page updates.

Key Features

  • Universal Data Connectors: Handles multiple catalog formats (XLSX, TXT, CSV, MySQL) and converts them into a standardized format for processing.
  • Smart Tagging for Auto Parts: Each product is tagged with unique attributes to ensure precise and relevant search results.
  • Real-time Search Updates: Provides an instant, dynamic user experience with real-time updates to search results as the user types.
  • In-memory Search Engine: Search results are quickly retrieved from memory to avoid disk bottlenecks, making data lookups extremely fast.
  • Modular System Architecture: The solution is divided into two key modules: one for data processing and another for user interface generation, allowing for easy scaling and maintenance.

Our Approach

Identifying Key Challenges and Defining the Solution
Azati’s team recognized the need to transition from multiple disconnected catalogs to a unified system. After analyzing the challenges, we decided to build a modular system, splitting it into data processing and user interface modules.
Developing the Data Processing Module
The team developed custom parsers for various document types and sources, ensuring data could be extracted efficiently and accurately. For large databases, an algorithm was built to asynchronously request data from multiple sources, reducing delays.
Leveraging React for Dynamic UI
For real-time user query updates and seamless page content modifications, Azati chose React for the front-end framework, ensuring the user interface could dynamically adjust without browser reloads.
Optimizing Search with Enhanced Algorithms
An advanced pairwise comparison algorithm was used as the core of the search engine, significantly improving accuracy and speed in search results.

Project Impact

Azati’s intelligent search engine improved the client’s inventory search process, drastically reducing lookup time and enhancing the accuracy of results. With in-memory storage and an advanced search algorithm, the engine can now return relevant search results in under 1 second. This solution enables the client to manage their large catalog effectively and provide a faster, more efficient user experience.

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