Whether online or stationary: product data determines visibility, conversion and sales. However, it is often incomplete, inconsistent or not even digitally available - scattered across PDFs, images, websites or old catalogs.
With modern AI technologies such as data cleansing, content mining and intelligent crawling, such information can be automatically indexed, structured and made usable for e-commerce or PIM. What used to be tedious manual work is now a scalable data process.
More than just tidying up: data cleansing
Everyone is familiar with classic data cleansing: removing duplicates, standardizing spellings, completing fields. However, the demand for data quality is much higher today - especially when it comes to automated export to web stores, marketplaces or print systems.
Modern data cleansing processes start earlier:
- They automatically check data for formal and logical plausibility.
- They detect incorrect units, contradictory measurements or inconsistent categorizations.
- They automatically evaluate data quality based on defined rules.
The result: cleanly structured, consistently validated product data that not only works better internally - but also impresses externally.
Content mining: making information visible that was previously hidden
A lot of relevant product information is not available as structured data, but "hidden" in PDFs, old catalogs, website texts or even image material. Such sources can hardly be evaluated efficiently by hand.
This is where content mining comes into play:
- New OCR methods digitize content from PDF documents, technical drawings or image material.
- NLP (Natural Language Processing) understands natural language and extracts precise product features.
- Image analysis recognizes colour variants, form factors or visually differentiating features - particularly relevant for product range images or style worlds.
Intelligent transformation then converts the extracted content into a usable, PIM-compatible format - ideal for structured further processing.
Crawling: Automatically capture product information from external sources
Intelligent crawling also helps to automatically obtain information from external platforms - for example from
- Manufacturer and supplier sites
- marketplaces
- Online catalogs and price lists
- Archives and data pools
Important: The targets of the crawling - i.e. the platforms or companies concerned - should be informed in advance about the data collection and give their consent. This ensures that crawling is not only technically efficient, but also legally and ethically compliant.
The AI specifically recognizes relevant content, filters out duplicate or outdated information and documents changes in real time. This keeps the database not only up-to-date, but also consistent and auditable.
Why the combination makes all the difference
Each of these technologies has advantages on its own. However, the approach only becomes really powerful in combination. The result is an end-to-end process - from data procurement to structured provision.
Typical advantages of the combined application:
- Completeness: No relevant content goes unnoticed - regardless of the format.
- Consistency: Content is automatically standardized, even for complex product ranges.
- Speed: New content is available online or in the PIM more quickly.
- Scalability: Even tens of thousands of articles can be processed efficiently and rule-based.
- Relief for the team: bottlenecks in human resources are effectively compensated for by automated processes.
Practical example: From PDF catalogs to a PIM-ready database
A medium-sized manufacturer was faced with the task of preparing around 15,000 items for digital channels. The starting point: printed catalogs, PDFs, images and a few technical Excel spreadsheets.
The solution approach:
- OCR/Vision AI analyzed the PDFs and extracted tables, descriptions and technical data.
- NLP recognized characteristics such as dimensions, materials and areas of application from continuous text.
- Image analysis supplemented visual data points.
- Crawling retrieved missing information directly from supplier sites.
All information was structured, cleansed and transferred to the target system - including an automatic check for completeness and formal consistency. The result: several months of data entry work saved - and a market-ready data set in record time.
Fields of application in practice
The possible applications are many and varied:
- Digitization of product ranges for manufacturers with catalogue-based databases
- Product data migration for PIM/ERP changes
- Marketplace connection with error-tolerant, automated data preparation
- Attribute enrichment for SEO, filter logic or specific touchpoints
- Onboarding processes for new suppliers or data pools
In short: AI-supported automation helps wherever data is not immediately ready for use.
Conclusion: intelligent data creates a real head start
The pressure on companies is growing: product ranges are changing faster, requirements are increasing - and digital channels expect clean, comprehensive data in real time. With data cleansing, content mining and automated crawling, a supposed "clean-up project" becomes a strategic lever:
for efficiency, speed and sustainable data quality.