Solving the Parts Chaos: AI-Driven Taxonomy and Visual Classification for OEM and Aftermarket Supply Chains

In today’s parts ecosystem, confusion reigns. From OEMs to aftermarket distributors, everyone seems to have their own naming conventions, product codes, and data structures. A single physical part might have multiple names, numbers, and categorizations—depending on who you’re buying it from. The result? Frustration, inefficiency, lost sales, and costly support issues.

We’ve built a system to eliminate that chaos.

Our patented Parts Classification and Search System is designed to bring order to disorganized, unstructured, and inconsistent supplier data. By standardizing parts classification across OEM and aftermarket catalogs—and combining that with powerful visual and contextual matching—we’re changing how the industry manages, organizes, and searches for mechanical, electronic, and technical parts.

✅ The Problem: Too Many Systems, No Shared Language

Every supplier, OEM, or distributor has their own way of organizing data:

• Different names for the same part

• Non-standard categories

• Confusing diagrams

• Poorly grouped systems (e.g., aviation and marine parts side-by-side)

• No intuitive way for users—especially non-technical ones—to know what will actually fit their machine

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Even the largest marketplaces suffer from these issues, leaving customers to guess, search endlessly, or abandon their purchase entirely.

🧠 The Solution: Unified Taxonomy + Matching Engine

Our system solves this by providing a universal taxonomy—a structured classification framework that any vendor’s parts can be mapped into, regardless of how their original data was formatted.

We’ve developed a four-tier taxonomy structure:

1. Asset Class – The industry (e.g., Automotive, Aviation, Lawn Care)

2. Type – The machine or product type (e.g., Mower, Generator, Engine)

3. Master System – The major system within the machine (e.g., Fuel System, Suspension)

4. Subsystem – The specific functional component (e.g., Gas Cap, Axle, Carburetor)

   
   

Each Subsystem is unique—and acts as the “anchor” for mapping parts consistently across vendors.

⚙️ How It Works

Here’s how our system transforms messy vendor data into clean, usable structure:

1. Build the Taxonomy

We start by creating a master taxonomy using real-world language—terms familiar to technicians and consumers alike (e.g., “Engine” instead of “Engine@LeftSide123”).

2. Ingest Supplier Data

We clean, simplify, and normalize incoming data by:

• Removing irrelevant part fields

• Eliminating special characters and manufacturer noise

• Standardizing formats

  
  

3. Match Against the Taxonomy

Our AI-powered matching engine compares the vendor’s simplified data against the master taxonomy using two logic paths:

• Exact Matches – If fields like “Gas Cap” match perfectly, the system applies the proper classification.

• Contains Matches – If a supplier uses “Engine Left Side,” the system detects that it contains “Engine” and applies the correct system name.

  
  

4. Apply Structure

Once matches are made, the correct Asset Class, Type, Master System, and Subsystem are backfilled onto the original parts list—resulting in clean, accurate, and searchable parts catalogs.

🔍 Visual Search: Find Parts by What You See

Beyond textual classification, we’ve also enabled visual search. Users can locate parts based on image pattern matching rather than relying solely on written descriptors. This makes it possible to:

• Search diagrams and technical drawings without typing keywords

• Match parts by shape, location, or system context

• Enable technicians and non-technical users to find the correct part visually

  
  

It’s the equivalent of reverse image search—but for mechanical systems.

📈 Real-World Impact

Our system delivers measurable benefits across the supply chain:

Challenge	Old Way	With Our System
Searching for parts	Trial-and-error browsing, poor filters	Standardized filters + visual matching
Cross-vendor consistency	None	Full normalization
Labor-intensive classification	Manual, slow, error-prone	Automated and AI-driven
Non-technical buyer confidence	Low – leads to abandoned carts	High – intuitive categories and naming
Time to deploy structured catalogs	Months	Days or weeks

🧩 System Architecture Overview

Our solution includes the following key components:

• Parts Classification Server – Core engine for processing supplier data

• Taxonomy Creation App – Builds and manages structured classifications

• Matching Engine – AI that maps unstructured data into the taxonomy

• Supplier Data Integrations – Accepts input from multiple OEM/aftermarket sources

• Final Classifications – Unified, searchable, industry-standard parts catalogs

  
  

🛠️ Designed for the Industry. Built for Scale.

Whether you’re a supplier looking to standardize your data, a marketplace integrating multiple catalogs, or a service organization searching for exact-fit parts—our system brings intelligence and structure to an otherwise fragmented ecosystem.

We’re not just improving parts data. We’re reengineering how it’s organized, searched, and sold.