Insights on product data intelligence.
Notes on taxonomy, data quality, and preparing product data for AI enabled commerce.
Measuring Product Data Quality: The Seventh Dimension
Fill rate is not product data quality. The six dimensions to measure, the seventh that decides if a product gets found and bought, and how to score yours.
What Product Data Enrichment Actually Requires
Product data enrichment is not just adding content. It is generating the right values for each category and validating them before they ship.
Product Taxonomy: How to Classify Products into the Right Category
A practical guide to product taxonomy in retail: how category trees work, how to pick the right node for a product, and worked examples from socks to cables.
AI Agents Shop on Attributes. The Depth and Accuracy of Your Product Data Decides What They Buy.
When AI agents do the shopping, the decision is made on attributes. The depth and accuracy of your product data determines what a machine can find, trust, and buy.
Tired of Tedious Templates? How AI Automates Product Content Template Completion
Template fatigue slows product launches. See how atronous uses AI automation to complete product content templates across marketplaces, accurately and fast.
Commerce Meets Intelligence: Agentic Commerce and the Golden Catalog
Shopping is shifting from human browsers to AI agents that compute over structured data. Here is how atronous makes product data agent-ready.
Inside Our Classification Pipeline: Computer Vision, Embeddings, and Similarity Scoring
An engineering look at how atronous classifies product images: object detection, embeddings trained on a retailer's own category tree, and audited matches.
Schematic Extraction v0.1.0: Architecture, Enhancements and Future work
How atronous's PDF Schematic Extractor pulls clean diagrams from cluttered product documents using PyMuPDF, OpenCV, Tesseract, and Gemini, plus what is next.