AI app for product development · no coding needed
Product taxonomy cleanup
Technical attributes and unit consistency within one complex product niche.
Made for: Catalog operations teams at industrial suppliers
What it does for you
The problem
Inconsistent categories and attributes block discovery and reporting.
What it gives you
Clean taxonomy and product mappings
What you give it
Product catalogspecificationsapproved taxonomy
How it works, step by step
- Map existing labels
- Propose canonical categories
- Normalize units
- Flag ambiguous products
- Preserve original values
- Export approved mappings
What you see on screen
- Category tree
- attribute mapping
- review queue
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Product taxonomy cleanup with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Product taxonomy cleanup with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links1 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare21 KB
- prompt-vps.mdThe same build on your own server (Docker)21 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
For catalog operations teams at industrial suppliers, turn product catalog, specifications and approved taxonomy into clean taxonomy and product mappings. Address the recurring problem: inconsistent categories and attributes block discovery and reporting. The pilot measures approved mapping accuracy and attribute completeness against the buyer's current method, before the larger build.
Import a limited collection, define canonical fields, suggest tags or mappings, review uncertain records, publish approved items, search and reuse them, and request periodic owner updates. Start with product catalog, specifications and approved taxonomy and finish with clean taxonomy and product mappings.
How the AI works
Suggest classifications, semantic tags, duplicate candidates and field mappings. Preserve original values. Use explicit validation for identifiers and units. Human stewards approve ambiguous merges and factual changes.
Safeguards
Use consented research and preserve contradictory evidence. Separate observed user behavior, proposed explanations and untested product assumptions. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
What to build first
Begin with catalog operations teams at industrial suppliers and one recurring use case. Build the first two modules: map existing labels; propose canonical categories. Provide operator assistance for the third module: normalize units. Deliver clean taxonomy and product mappings through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
What it can connect to
Product feedback, authorized interviews, usage exports and requirement records. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. These are candidate integration categories, not verified supported connectors.
The screens in detail
Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. In this product, the first view is category tree, followed by attribute mapping and review queue.
