Product data from brands, checked before it's published

1. 01

   Each customer's data is different

   Each customer wants its own fields, classifications, and formats, so the data model can't be fixed in code.

2. 02

   Brands' data has to be right

   Product data arrives from brands and customers, and mismatched codes, duplicates, or inconsistent names must be caught before the data is used.

3. 03

   Many products, many hands

   Products pass through capture, creative, and checking teams, and bulk changes touch many products at once, so the work needs structure.

A product, from loadsheet to API

1. 1

   Load

   A customer's products arrive on a loadsheet and become a job. Duplicate GTINs and missing required cells are flagged as it's loaded.

2. 2

   Match

   Each product is matched to its baseline record. SKU mismatches and duplicates are errors to resolve, and brand or sub-brand names that differ from the standard are warnings.

3. 3

   Capture

   Products are split into batches for capture. AI suggests descriptions, categories, features and benefits, and keywords, and reads text and nutritional tables from product images.

4. 4

   Check

   Each batch passes capture QA, then creative and creative QA, before it goes to review.

5. 5

   Create

   Reviewed batches wait for their products to be created, then the batch is completed and the job's progress moves on.

6. 6

   Publish

   The customer's systems read the products through the client API, with each API key returning the fields and layout of that customer's export template.

• Jobs, batches & QA

  Loadsheets become jobs, jobs are split into batches, and batches move through capture, capture QA, creative, creative QA, and review, so everyone can see where each product stands.

• Custom-field data model

  Fields are configured, not coded: 19 types from text, numbers, and dates to GTINs, SKUs, and taxonomy levels, owned by the customer, the brand owner, or the client.

• Derived fields

  Aggregate fields combine several fields into one. Transform fields change case, trim or replace with patterns, or apply standard brand names. Inherited fields take their value from an ordered list of sources, chosen by filter rules.

• Accuracy checks

  Loadsheet checks, baseline matching, code mismatch and duplicate errors, brand name warnings, and a daily check for possible duplicate products.

• Template-driven client API

  Export templates define the fields and layout each customer receives. API keys can be tied to a template, and each key sees only the values it's allowed to.

• Brand owner imports

  Brand owners send product data over their own API. It's validated, then queued for import against their products.

• AI-assisted content

  AI drafts descriptions, categories, features and benefits, and keywords during capture, and reads text and nutritional tables from product images.

• Queued bulk work

  Imports, exports, product updates, and image fetches run in background queues. Six import types, from linking GTINs to pack configuration set-up, are processed and recorded.

• Capture & creative teams

  • Batches of products ready to work on
  • AI-drafted descriptions and keywords to review
  • Text and nutritional tables read from images

• QA & review

  • A QA stage after capture and after creative
  • Errors and warnings listed against each product
  • Value history and audits on product changes

• Admin team

  • Fields, dictionaries, and templates set up in the portal
  • Bulk imports queued instead of done by hand
  • Sign-in with their company accounts

• Customers & brand owners

  • Product data through an API in their own shape
  • API keys limited to their own data
  • An API for brand owners to send product data in

1. 2021

   Foundation

   Jobs, loadsheets, capture and QA stages from the first months, and the client API by July.

2. 2022–2023

   Templates & brands

   Export templates to shape each customer's data, an API for brand owners, and AI-assisted content from 2023.

3. 2024–2025

   Completeness

   Completion metrics per customer and an internal API that publishes the import schema brand owners build against.

4. 2026

   Modernised

   An AI data assistant added for the client's teams.

• Business rules written as plain functions over loaded data, so the API's rules on who sees what are tested without a database.
• The client API returns a value only when the rules say a caller may see it, so anything unexpected stays hidden rather than shown.
• Pattern-based transforms are guarded against invalid patterns and time limits, so one bad rule can't hold up the rest.
• One definition of required fields drives both the import checks and the schema brand owners build against, so the two can't drift apart.
• Bulk work runs in queues with a status per item and a recorded snapshot of what changed, and image fetches retry before giving up.
• About 430 automated tests, and the roughly 300 that need no database run before every deployment to the development environment, stopping it if any fail.
• Kept current with regular technology, library, and framework updates, along with ongoing performance and security improvements.

• .NET
• ASP.NET Core
• SQL Server
• Angular
• Active Directory
• GitHub Actions