It makes a fast migration possible.
Foodline AI migrates your data itself, and migration and cutover take 3 to 4 business days on clean data (compare/legacy-erp). Cleanup is how your item file gets there.
Product data cleanup
Product data cleanup for food distributors: names, images, descriptions, allergens and categories
CategoryFRESH_HERBS → Fresh Herbs
NameCHIVE FRSH 1/LB BNCH → Fresh Chives, 1 lb Bunch
Item numberShown on every listing
DescriptionShort, factual bullets
Allergens and storageStructured fields
ImageSquare WebP, brand-matched background
Illustrative example, not a client's data.
Short answer: Foodline AI's product data cleanup service turns a raw ERP item file into ecommerce-ready product data. We clean product images, rewrite all-caps item names as buyer-friendly titles, write short AI-ready descriptions, add allergen and storage metadata, and replace raw category codes with clean labels, for every product in the batch. We consult on what your catalog needs and fix it. Clean product data is what makes a 3 to 4 business day migration to Foodline AI possible, and it is what your B2B store and AI search run on.
What is food distributor product data cleanup?
Product data cleanup is the work of turning the item records in a distribution ERP into product data that customers can shop and software can read. ERP item files are built for the order desk and the warehouse. Names are abbreviated and in capitals, categories are system codes, images are missing or inconsistent, and allergen and storage details live on the manufacturer's spec sheet instead of on the item. That data works on a pick ticket. It doesn't work in a B2B online store or for an AI assistant answering a buyer's question.
The five layers we clean, for every product in the batch
1. Product images
High-quality product images on category-specific color backgrounds, matched to your brand palette. Every image is standardized to a square, converted to WebP so pages load fast, and trimmed of gray borders. Each product gets one image.
2. Human-readable product names
Raw, all-caps item names from your ERP, such as NECS entrée item names, are rewritten as professional, buyer-friendly titles. Your item numbers stay on every listing, so the order desk, the warehouse and customers still speak the same language.
3. AI-ready product descriptions
Short, factual, SEO-optimized bullet descriptions for each item. They are written to be read by your customers in the store and by the AI assistant when it answers questions or builds an order.
4. Allergen and storage metadata
Allergens, storage requirements, shelf life and prep notes, structured as metadata fields on each product page instead of buried in free text. Buyers can find what they need, and the AI assistant can read it.
5. Structured label taxonomy
Clean category and class labels replace raw ERP strings. For example, FRESH_HERBS becomes Fresh Herbs. A consistent taxonomy is what store navigation, filters and search are built on.
Before and after
Illustrative examples only. Apart from the FRESH_HERBS category label, these item names, item numbers and values are made up to show the format; they are not a client's data.
| Layer | Before: raw ERP export (example) | After: ecommerce-ready (example) |
|---|---|---|
| Category label | FRESH_HERBS | Fresh Herbs |
| Product name | CHIVE FRSH 1/LB BNCH | Fresh Chives, 1 lb Bunch (Item #10452) |
| Product name | CHS MOZZ SHRD LMPS 4/5# | Shredded Low-Moisture Part-Skim Mozzarella, 4 x 5 lb (Item #20381) |
| Description | None | Shredded low-moisture, part-skim mozzarella. Four 5 lb bags per case. Keep refrigerated. |
| Allergen and storage metadata | None, or mixed into the item name | Allergens: Milk. Storage: Refrigerated. Shelf life and prep notes as their own fields |
| Product image | Missing, mixed sizes, gray borders | One square WebP image on a category color background matched to your brand |
Allergen details should always match the manufacturer's label for the exact product you stock.
How the service works
- 01Consult. We look at your item file with you and agree what each layer needs for your catalog and your brand.
- 02Clean. We rewrite names and descriptions, build the taxonomy, structure the allergen and storage fields, and process the images.
- 03Fix. Problems we find along the way, such as duplicate items, wrong categories or names that don't match the pack, get fixed rather than carried forward.
- 04Deliver. All five layers are delivered for every product in the batch.
Why clean product data matters
It powers your B2B store.
Foodline AI's store runs on the ERP record (ecommerce-erp), so clean names, images, categories and allergen fields show up for customers the moment products go live.
It powers AI search and the assistant.
Structured descriptions, labels and metadata are what let search and the AI assistant find the right product and answer questions about it.
It carries over to everything else.
The same clean record feeds pick tickets, invoices, order guides and reports.
It stays clean after go-live.
For Foodline AI customers, migration and ongoing data management are included in the subscription. If a source record needs unusually heavy cleanup, we scope the right support tier with you before work begins (data management).
Related guides
- How to migrate from NECS entrée, step by step, including what to clean before you export
- NECS entrée alternative: entrée vs Foodline AI
- B2B ecommerce and ERP in one system
- Replace QuickBooks with one system built for food distribution
NECS and entrée are trademarks of their owner. Foodline AI is not affiliated with NECS, Inc.
Frequently asked questions
What is food distributor product data cleanup?
It's the work of turning raw ERP item records into ecommerce-ready product data. Foodline AI cleans five layers for every product in the batch: product images, human-readable product names, AI-ready descriptions, allergen and storage metadata, and a structured category and class taxonomy.
Can you clean up NECS entrée item names?
Yes. Raw, all-caps entrée item names are rewritten as professional, buyer-friendly titles, and your item numbers stay on every listing. Raw category strings are replaced with clean labels, so FRESH_HERBS becomes Fresh Herbs.
What allergen and storage metadata do you add?
Allergens, storage requirements, shelf life and prep notes, structured as metadata fields on each product page so customers and the AI assistant can read them. Allergen details should always match the manufacturer's label for the exact product you stock.
What do you do to product images?
Each product gets one high-quality image on a category-specific color background matched to your brand palette, standardized to a square, converted to WebP for fast loading, with gray borders removed.
How does product data cleanup help a migration to Foodline AI?
Foodline AI's data migration and cutover take 3 to 4 business days on clean data. Cleanup is how your item file gets there, and the same clean data then powers your B2B store, AI search and the AI assistant.
How much does product data cleanup cost?
Pricing isn't published. For Foodline AI customers, migration and ongoing data management are included in the subscription, and if a source record needs unusually heavy cleanup, we scope the right support tier with you before work begins. Book a walkthrough and send an item export to start.
See it run on your own numbers.
Thirty minutes. We load a slice of your catalogue and show you the routines firing against your real order history, not a canned demo.
Book a walkthrough