Let AI handle repetitive work
AI suggests categorisation, attributes, translation and content.
AI Product Data Automation
Automate the product data work you still do manually.
A lot of product data work consists of recurring tasks. AI categorises, fills in attributes, translates and enriches. Your team keeps control over quality and exceptions.
Organisations automating product data with ConnectingTheDots
Less manual work, more output
What does controlled AI automation deliver?
Stay in control of the final decision
Every suggestion can be reviewed, edited and reversed.
Apply the same rules everywhere
Use fixed instructions and validations across categories, languages and brands.
Sound familiar?
AI only speeds things up once the foundation is right.
Loose prompts don't yet add up to a scalable product data process.
Thousands of products are waiting for content
Writing, translating and filling in content costs teams a lot of time, every single day.
Source data isn't consistent
When fields, values and structures differ, AI output becomes less reliable too.
No one knows what's good enough
Without fixed rules and review steps, it remains unclear what can be processed automatically and what needs human review.
Every task starts with a new prompt
Without fixed instructions and flows, automation stays dependent on one-off actions.
How we approach it
AI as an accelerator in a controlled flow.
Give AI the right foundation
Use your data model, category context and validated source data as the starting point.
- Fixed fields and values
- Category context
- Validated source data
Let AI do the groundwork
Automate repeatable tasks in your product data flow.
- Categorising
- Suggesting attributes
- Translating and enriching
Only let good data through
Combine AI with validations, review and full traceability.
- Validation per field
- Human approval
- Reversing changes
How customers use AI without losing control.
These organisations automate product data work without losing control over quality and exceptions.
Processing product data from many suppliers more quickly and preparing it for further distribution.
- 50+ supplier feeds
- Less manual processing
Checking and enriching supplier data before products move further into the publication flow.
- Controlled enrichment
- Faster publication
Processing large product volumes consistently across multiple brands, sources and categories.
- 2.5M+ SKUs
- Multiple brands and sources
Managing product data for multiple sports brands centrally and pushing it automatically to webshops, retailers and marketplaces.
- Multiple sports brands
- Automated channel feeds
Importing, structuring and preparing supplier data for IT products for sale automatically.
- Automated import
- Fast publication
Technical architecture
How AI works in the product data flow.
01 · Context
Give AI the right foundation
Use the data model, source data and instructions to place every product in the right context.
Technical
- Data model
- Prompt context
- Source selection
AI bonus
- Identifies relevant source information
02 · Classify
Place products in the right category
Automatically link products to your existing taxonomy.
Technical
- Taxonomy
- Confidence scores
- Review rules
AI bonus
- Suggests categories with a confidence score
03 · Extract
Extract attributes from source data
Turn titles, descriptions and other unstructured content into usable attributes.
Technical
- Entity extraction
- Units
- Source trace
AI bonus
- Recognises attributes and values in free text
04 · Enrich
Fill in missing information
Let AI suggest values within the structure and rules of your data model.
Technical
- Attribute rules
- Derived values
- Templates
AI bonus
- Generates suitable values and content suggestions
05 · Translate
Translate using product context
Preserve terminology, category context and brand style per language and market.
Technical
- Glossaries
- Locales
- Brand rules
AI bonus
- Adapts translations to the product, category and brand
06 · Validate
Only let good suggestions through
Test AI output against fixed rules before it's accepted or published.
Technical
- Validation rules
- Human review
- Audit trail
AI bonus
- Flags deviations and uncertainty
07 · Improve
Make each new run better
Reuse approved corrections and examples in subsequent product batches.
Technical
- Feedback
- Rule tuning
- Versioning
AI bonus
- Reuses approved examples as context
08 · Let’s talk
Where can AI speed up your product data work?
We look at which recurring tasks you can safely automate and where human control still matters.
- 30 minutes, straight to the point
- Not a standard demo
- Specific to your product data flow
Which product data tasks could be automated today?
We pick one recurring task and show where AI saves time and safeguards quality, and where human control remains necessary.
- See where AI saves time straight away
- Know which checks remain necessary
- Get a concrete automation proposal
Frequently asked questions.
Can't find your question? Get in touch
AI can speed up recurring product data work, such as categorising, suggesting attributes, extracting information from text, translating and enriching content. Always within the structure and rules of your existing product data flow.
Yes. Suggestions are traceable, can be checked against fixed rules and only become final after the review step you define.
That depends on the chosen AI setup and provider. We clarify upfront which data is processed, where it's processed and which agreements apply to storage and model training. Discuss your AI setup.
No. A clear structure helps, though. The more clearly your data model, source data and rules are defined, the more reliably AI can support you. See where your product data is ready for automation.
Yes. Start with, for example, one category, language or recurring task. If the results are good, you can extend the same approach to other products and processes.
Tasks that recur often and have clear inputs and rules are especially suitable. Think of categorising, filling in attributes, translating and preparing content. See in a demo where you could start.