From raw material to enriched content: The art and science of AI-based product onboarding

The Battle with Unstructured Supplier Data

Every company that works with suppliers is familiar with the problem: Product data is provided in various formats and qualities — sometimes as an Excel spreadsheet, sometimes as a PDF or even as unstructured text documents. This makes integration into existing systems a time-consuming challenge. But this is exactly where AI-based product onboarding comes in. With modern algorithms and automated workflows, Ainavio transforms chaotic raw data into a structured, error-free and enriched database.

Why is a standardized database crucial?

Without a clean and uniform structuring of product data, companies face serious problems:

• Inconsistent data Lead to incorrect product presentations and poor user experience.
• Incomplete information Make it difficult for customers to make decisions and can lead to purchase cancellations.
• Manual fixes Are time-consuming and costly, tie up resources and are prone to errors.

A well-thought-out, AI-supported onboarding process ensures that product data is available in the highest quality — up-to-date, consistent and automatically optimized.

How Ainavio's AI-powered product onboarding works

Ainavio uses multi-stage, AI-based processing to generate a “golden record” from heterogeneous supplier data — an error-free, fully enriched and standardized product database.

1. Data collection and classification

• Processing of structured (CSV, XML) and unstructured (PDF, email) data.
• Automatic categorization of product data according to predefined standards.

2. Duplicate check and data cleansing

• AI detects and removes duplicate or conflicting data.
• Consolidate variants to avoid duplicates.

3. Attribute and value normalization

• Standardization of measurement units, prices, and product specifications.
• Automatic addition of missing values through pattern recognition and external data sources.

4. Automated content enrichment

• Generation of SEO-optimized product descriptions.
• Insertion of relevant product attributes based on AI-supported analysis.

5. Quality Control and Approval

• Validation of data quality through AI models.
• If necessary, manual correction options for specific special cases.

From theory to practice: examples of the AI-supported onboarding process

Example 1: Automatic Harmonization of Measurement Units

One supplier delivers product dimensions in inches, another in centimeters. Ainavio's AI recognizes the discrepancies and automatically converts all information into the metric system to ensure a uniform presentation.

Example 2: Complete missing attributes

If a product data set is incomplete, AI can automatically add information from other sources. For example, if the material of a product is missing, it can be extracted from the description or image analysis.

Example 3 Duplicate Elimination

A retailer receives the same item from two different suppliers under slightly different names. Ainavio's AI recognizes the similarities and combines the data into a single data set.

These examples only show a small selection of the possibilities. In a live demo, we would be happy to show you how our AI works with your specific product data.

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