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Inventory Search Engine for Auto Parts Retailer

Azati designed and developed an AI-powered inventory search engine for auto parts retailers, enhancing traditional parts search algorithms. The solution helps customers find auto parts, automotive parts, and accessories quickly and accurately. It analyzes user input, looks for a specific entry in the auto parts inventory, and if the algorithm can’t find the requested item, it explores product characteristics and returns a list of similar automotive parts and accessories.

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≤1 second

average time to return search results

15K

objects were analyzed while developing a prototype

300K

attributes were generated from the sample dataset

All Technologies Used

Python
Python
Flask
Flask
React
React
Tornado
Tornado
Memcached
Memcached

Motivation

A Brazilian auto parts retailer with an online catalog of over 500,000 auto parts and 100,000 automotive accessories faced slow and inaccurate search results. Traditional search solutions could not handle the massive auto parts database, resulting in poor customer experience. Azati’s goal was to create an AI car parts search engine that optimized the process of finding auto parts online, improving both speed and accuracy. The new auto parts lookup software replaced the ineffective legacy system with a scalable solution capable of handling large datasets in real time.

Main Challenges

Challenge 01
Handling multiple catalog formats

The client’s auto parts inventory data was spread across XLSX, TXT, CSV, MySQL databases, and APIs. Azati developed universal data connectors to unify the catalog and standardize information. This enabled fast processing and indexing for auto parts search software.

#1
Challenge 02
Building an intelligent search engine

The parts search engine had to quickly analyze user queries, match them against product attributes, and return highly relevant results. Azati introduced AI-powered auto parts tagging with unique attributes, enabling accurate car part finder functionality.

#2
Challenge 03
Real-time data lookup and UI updates

The retailer required instant auto parts lookup while users typed queries. Azati implemented an in-memory search solution that bypassed database bottlenecks, ensuring real-time search updates for a seamless customer experience.

#3

Our Approach

Identifying Key Challenges and Defining the Solution
Azati recognized the retailer’s need to unify fragmented catalogs and improve auto parts search engine accuracy. The solution was a modular system, balancing search-engine databases for automotive data with a dynamic React UI.
Developing the Data Processing Module
Custom parsers and ETL pipelines were created for different sources (databases, spreadsheets, APIs). For huge automotive databases, asynchronous queries were implemented speeding up auto parts lookups across multiple systems.
Leveraging React for Dynamic UI
React enabled a fast auto parts search interface with live filtering, find parts autocomplete, and zero page reloads. This improved customer engagement in the auto parts eCommerce platform.
Optimizing Search with Enhanced Algorithms
Azati applied a pairwise comparison algorithm at the core of the engine, powering AI-powered parts search. This ensured fast, precise car parts search results and improved handling of complex user queries like “find engine for Honda Civic 2017”.

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Solution

01

Universal Data Connectors

Handles multiple catalog formats (XLSX, TXT, CSV, MySQL, APIs) and unifies them into a standardized structure. This ensures all auto parts and accessories data is consistent, clean, and ready for fast processing by the search engine.
Key capabilities:
  • Extract and normalize data from multiple sources
  • Provide a single, unified catalog for search operations
  • Enable fast indexing and retrieval for large datasets
  • Reduce errors caused by inconsistent catalog formats
02

Smart Tagging for Auto Parts

Every auto part is enriched with AI-powered unique attributes and tags. This allows the search engine to understand product characteristics, making search results highly accurate and relevant, even when queries are ambiguous or incomplete.
Key capabilities:
  • Assign unique AI-generated tags to every product
  • Categorize parts for more precise search results
  • Support complex queries like vehicle-specific part searches
  • Enable similarity-based recommendations for unmatched queries
03

Real-time Search Updates

As users type queries, the search results dynamically update without page reloads. This provides a responsive, interactive experience similar to Google search, keeping the user engaged and reducing friction in finding the right part.
Key capabilities:
  • Instantly display search results as the query is entered
  • Enable live filtering and autocomplete suggestions
  • Enhance customer engagement with a smooth interface
  • Reduce search abandonment by delivering fast feedback
04

In-memory Search Engine

Search queries are processed in memory to avoid disk read/write bottlenecks. This ensures extremely fast lookups, even for large datasets containing hundreds of thousands of parts and attributes, providing users with near-instant results.
Key capabilities:
  • Retrieve search results directly from memory for speed
  • Handle large datasets efficiently
  • Ensure consistent performance regardless of data volume
  • Support concurrent searches without latency
05

Modular System Architecture

The system is divided into two key modules: one for data processing and another for UI generation. This modular design allows for easy maintenance, scaling, and independent updates of search logic and interface functionality.
Key capabilities:
  • Separate concerns between data processing and UI rendering
  • Enable easier maintenance and updates
  • Allow scalable expansion of inventory without performance issues
  • Facilitate rapid deployment of new features

Business Value

Faster Auto Parts Lookup: The AI-powered search engine reduced search times to under one second, providing instant results and improving the overall shopping experience.

Higher Search Accuracy: Smart tagging and AI-driven algorithms ensured precise matches for auto parts and accessories, minimizing incorrect or irrelevant results.

Improved Customer Experience: Users can quickly find the parts they need across multiple formats and catalogs, enhancing satisfaction and engagement.

Scalable System Architecture: The modular design allows the retailer to handle massive datasets efficiently and expand the inventory without performance issues.

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