Automate Product Data Enrichment via AI Vision & Web Research
Automate the extraction and enrichment of product data from images using AI, saving hours of manual research and significantly improving the accuracy of inventory records.
Manually identifying product details from images and searching the internet for specifications is a time-consuming and error-prone process for surveyors. This workflow leverages AI vision and intelligent agents to automatically analyze product photos, perform web research, and enrich your Airtable records with accurate product attributes, saving countless hours of manual data entry.
Documentation
AI-Powered Product Data Enrichment with Vision and Web Research
Manually identifying product details from survey photos and researching specifications online is incredibly tedious and prone to human error. This n8n workflow automates this entire process, transforming raw image data into structured, enriched product information directly in your Airtable database.
Key Features
- Automated Image Analysis: Automatically extract details like descriptions, models, materials, colors, and conditions from product photos using advanced AI vision models.
- Intelligent Web Research: An AI Agent intelligently performs reverse image searches and scrapes relevant web pages to find missing or confirm existing product attributes.
- Airtable Integration: Seamlessly pull product images from Airtable and write back enriched data, including a status flag for processed items.
- Customizable Data Extraction: Define the exact product attributes you need, such as title, model, material, color, and condition, to match your inventory requirements.
How It Works
This workflow begins by scanning your designated Airtable base for new product photos that haven't been AI-processed. For each applicable row, an OpenAI Vision model analyzes the image to extract initial product attributes and a detailed description. An n8n AI Agent then takes this initial information and, using integrated tools for reverse image search (via SerpAPI) and web scraping (via Firecrawl), researches the internet to fill in any missing attributes or verify existing ones. Finally, the enriched product data is written back to the original Airtable record, marking it as processed.