Original project100K+ records92% accuracyGraph retrieval

E-commerce Product QA using Product Association Graph

A product intelligence system that answers compatibility, comparison, and discovery questions across more than 100,000 e-commerce records. Association-graph retrieval gives the model the right product neighborhood and achieved 92% answer accuracy in evaluation.

Origin: Original project
Role: Product, design & technology
Focus: Application
Engineering Area: AI & ML
Domain: Business & operations
Status: Prototyped

Why this should exist

Product questions depend on relationships that keyword search cannot see

Compatibility, alternatives, and comparisons require more than matching a query to one catalog record. This system uses a product association graph to retrieve the neighborhood that gives each question useful context.

Problem and product

The problems

01
Large catalogs hide compatibility and substitution relationships.
02
Vector similarity alone may retrieve related text without the right product connection.
03
Answers need evidence across multiple records without flooding the model with catalog data.

What the product delivers

Indexes and reasons over more than 100,000 product records
Models related products through a product association graph
Retrieves graph neighborhoods relevant to each customer question
Combines structured relationships with RAG-based context retrieval
Generates context-aware answers grounded in product data
Handles product comparison, compatibility, and discovery questions

Technology used

A stack chosen for the product

Python pipelines build a product association graph across more than 100,000 records. Graph retrieval selects relevant neighborhoods, RAG adds supporting product context, and an LLM generates a bounded answer.

RAGNLPGraph RetrievalProduct Association GraphVector SearchLLMPython

Architecture

How the system holds together

01
Catalog graph
Products become nodes connected by observed association, compatibility, and comparison signals.
02
Hybrid retrieval
Graph neighborhoods and vector search combine structure with semantic relevance.
03
Grounded answer service
The model receives a bounded evidence set and generates an answer tied to product data.

Estimated goals

A product intelligence layer for discovery and support

The same association graph can improve customer questions, comparison journeys, and catalog operations when its accuracy remains measurable.

01
Evaluate graph retrieval across compatibility and comparison question types.
02
Add freshness checks as product data changes.
03
Test conversion and support impact without weakening answer accuracy.

Future goals are directional estimates and depend on user validation.

Extras

Product perspective

Primary audience: E-commerce discovery and support teams
Measured signal: 92% answer accuracy across the reported evaluation
Product edge: Structured product relationships narrow the evidence before generation

Execution principle

Validate the narrow customer promise first. Add scope only when evidence shows that it improves the outcome.

Full project notes

Product and engineering deep dive

The project overview adds a concise presentation layer. The complete product details remain below.

From intent to implementation

A product direction shaped around a real use case.

A product question-answering system built over more than 100,000 e-commerce records, using association-graph retrieval, RAG, and context-aware answer generation to achieve 92% answer accuracy.

01 · The product response

What people can do with it

Indexes and reasons over more than 100,000 product records
Models related products through a product association graph
Retrieves graph neighborhoods relevant to each customer question
Combines structured relationships with RAG-based context retrieval
Generates context-aware answers grounded in product data
Handles product comparison, compatibility, and discovery questions
Achieved 92% answer accuracy in evaluation

Project sponsorship

Fund this project—from prototype to production.

Help move E-commerce Product QA using Product Association Graph from a promising prototype to a reliable production release.

Design validation and user research
Production infrastructure and security
Testing, accessibility, and launch readiness

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