From shop counter to online catalogue: Inside the DTCommerce project

A Slovenian team set out to build open-source tools that help small retailers go digital easily, by importing product descriptions from a spreadsheet into an online shop – with AI-enhanced descriptions and images, in just a few clicks

For small to medium sized Brick-and-Mortar retailers, the move from physical shops to e-commerce is a long and cost intensive process. These businesses typically have an accounting system with a list of products, perhaps a supplier’s website with technical specifications, and neither the time nor the budget to manually write product descriptions, source images, and populate an online shop for hundreds or thousands of items. The result is that many small retailers either delay their digital transition or end up with online catalogues that are sparse, poorly described, and unappealing to customers.

The main challenge is not a lack of products but a lack of digital product content. A physical shop’s inventory usually exists as a list of names, SKU (stock keeping units) codes, and prices in accounting software. An online shop needs other specifications : well-crafted product descriptions, high-quality images, metadata, and engaging presentations. Creating this content  quickly becomes a substantial undertaking.

The DTCommerce project, carried out by the Slovenian company ZenLab under the European OpenWebSearch.eu project funding, set out to solve this problem with an automated solution. The idea is simple: take the product list a shop already has, find the corresponding product information on the web, enhance the descriptions using AI, and deliver the result as a ready-to-use online shop – with minimal manual effort.

The Approach: Automated Extraction and AI Enhancement

The DTCommerce system operates in two stages. The first stage is a web crawling process that, given a list of product URLs from supplier or manufacturer websites, automatically extracts the key product information, such as: title, description, imagery, price, and technical specifications. The crawler is built on Scrapy, a well-established open-source web scraping framework, and includes support for structured data formats (JSON-LD) as well as domain-specific extractors for particular target sites.

The second stage is where AI comes in. The raw product descriptions extracted from supplier websites are often technical, dry, and written for a trade audience rather than end consumers. DTCommerce feeds these descriptions to an AI language model (Perplexity AI’s sonar-pro), which rephrases them into clearer, more engaging copy while preserving every technical detail – dimensions, model numbers, and specifications. The original description is retained alongside the enhanced version, so nothing is lost. The result is a set of enriched product records in a standardised format, ready to be imported into an e-commerce system.

From Pipeline to Plugin: A Few Clicks to a Full Shop

To make the pipeline usable for non-technical shop owners, the ZenLab team built a WordPress/WooCommerce plugin that wraps the entire workflow into a simple administrative interface. The process works as follows: the shop owner exports a product list from their accounting software as an Excel file and uploads it to the plugin. The plugin creates basic product entries in WooCommerce, sends them to the enrichment service, and automatically populates each product page with enhanced descriptions and images – all without requiring the shop owner to edit a single product manually.

An Honest Detour: When the Open Web Index Didn’t Have What Was Needed

DTCommerce was originally designed to use the Open Web Index (OWI) as its primary data source for finding product information across the web. The vision was that a shop owner could provide a product name or SKU code, and the system would search the OWI to find matching products on supplier and manufacturer websites, automatically retrieving descriptions and images.

In practice, the specific e-commerce sites that the project’s use cases required were not present in the OWI at the time of development. This is not surprising: the OWI is still being built, and its coverage of niche commercial sites – particularly smaller Slovenian B2B suppliers – is not yet comprehensive. The team adapted by switching to direct web scraping of predefined supplier URLs, which allowed the project to deliver its core functionality on schedule.

Why the project matters

DTCommerce addresses a real and widespread problem. Across Europe, millions of small retailers face pressure to establish an online presence but lack the resources to do so effectively. By automating the most labour-intensive part of the process – creating digital product content – the project lowers the barrier to entry in a meaningful way. The fact that the tools are open source and built on widely used platforms (WordPress, WooCommerce, Scrapy) means they are accessible to a broad audience and can be adapted to different markets and product domains.

The project also illustrates a type of application that open web search infrastructure is well suited to support. The ability to search an open web index for product information – matching a local shop’s inventory against the broader web – is precisely the kind of use case that depends on open, non-proprietary access to web data. As the OWI matures, tools like DTCommerce stand to benefit directly. The project overall also demonstrates both the potential of the OWI-based approach and its current practical limits.

Final Outlook