AI app for marketing · no coding needed
E-commerce merchandising assistant
Specification accuracy and consistent comparison attributes for one product niche.
Made for: Catalog managers at specialist online stores
What it does for you
The problem
Inconsistent product information makes comparison difficult.
What it gives you
Reviewed product catalog content
What you give it
Verified product specificationscatalog taxonomy
How it works, step by step
- Normalize attributes
- Draft factual descriptions
- Flag missing specifications
- Suggest categories
- Build comparisons
- Export approved listings
What you see on screen
- Product table
- attribute review
- storefront preview
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 E-commerce merchandising assistant 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 E-commerce merchandising assistant 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 build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare22 KB
- prompt-vps.mdThe same build on your own server (Docker)22 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data194 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 managers at specialist online stores, turn verified product specifications and catalog taxonomy into reviewed product catalog content. Address the recurring problem: inconsistent product information makes comparison difficult. The pilot measures attribute completeness and listing correction rate 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 verified product specifications and catalog taxonomy and finish with reviewed product catalog content.
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
Verify product claims and permissions. Distinguish observed campaign results from causal explanations and keep customer data collection authorized. 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 managers at specialist online stores and one recurring use case. Build the first two modules: normalize attributes; draft factual descriptions. Provide operator assistance for the third module: flag missing specifications. Deliver reviewed product catalog content 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
Approved brand material, campaign exports and authorized customer research. 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 product table, followed by attribute review and storefront preview.
