Product catalogues, written and digitised by AI

1. 01

   Copy for every product

   Each product needs descriptions, a headline, features and benefits, keywords, and a category, written consistently in the right tone and language.

2. 02

   Data printed on the pack

   Ingredients, allergens, certifications, and nutrition tables only exist on packaging, and nutrition data needs standard codes before other systems can use it.

3. 03

   Fitting into existing systems

   Teams already work in spreadsheets and their own tools, so the results have to go in and come out without copying and pasting.

A catalogue, from spreadsheet to results

1. 1

   Choose

   The team picks the work, such as marketing descriptions, headlines, and categories, with tone, length, and translation settings. Anything a choice depends on, like the marketing description a headline is written from, has to be included.

2. 2

   Try

   A single product can be run straight away in the portal to check the output before committing a catalogue. If a test run fails, its credits are refunded automatically.

3. 3

   Import

   The catalogue goes in from the Excel template or over the API, up to 10,000 products a batch. The cost is estimated and checked against the workspace's credits before anything starts.

4. 4

   Process

   Each AI step has its own background queue, checked every 20 seconds and working on up to 10 products at once. Steps run in order, and each is tried up to 3 times before it's flagged for attention.

5. 5

   Check

   Results are tidied before they're stored: UK spelling and units applied where the workspace asks for them, category paths built from the taxonomy, and nutrient codes checked against the GS1 list.

6. 6

   Deliver

   When the batch finishes, portal users are emailed, any webhook is called, and the results are ready in the portal, as an Excel export, or from the API.

• E-commerce copy

  Marketing descriptions, short descriptions, headlines, features, attributes and benefits, and SEO keywords. Each product gets three alternative marketing descriptions, in one of five tones, at a shorter, default, or longer length.

• Taxonomy categorisation

  Products are matched to the Google or GS1 taxonomy. The ten closest categories are found by meaning, AI chooses the most specific, and the full category path comes from the taxonomy itself.

• Image digitisation

  Text extraction, table digitisation, and nutrition table digitisation from photos and scans, returned as editable text and structured tables.

• Back-of-pack extraction

  From one or more photos of a pack: brand, net quantity, serving size, ingredients, allergens, storage and usage instructions, origin, claims, and 11 dietary and certification flags such as halal, kosher, and vegan. Barcodes are decoded from the images too.

• Standard nutrient codes

  Each nutrition row gets a GS1 T4073 nutrient code, checked against all 1,083 codes in the list. Known mistakes are corrected, unlisted nutrients get a consistent code, and units are normalised.

• Rewrite & translate

  Rephrase in a chosen tone, summarise to a target length, or translate into 47 languages. E-commerce batches can be translated as they're written, and English copy can follow UK spelling and metric or imperial units.

• API, webhooks, Zapier & Make

  A REST API with an interactive explorer, and single-product and batch endpoints for each type of work. Batches can call a webhook when they finish, a Zapier app offers 16 actions, and it connects to Make too.

• Credits & billing

  Each batch is costed before it starts and checked against the balance. Credits are bought by card with bonus tiers, bundles expire, and workspaces can have monthly credits and their own pricing.

• E-commerce & content teams

  • Three marketing description drafts per product
  • Tone, length, and language chosen per batch
  • Results to review in the portal or export to Excel

• Product data teams

  • Nutrition tables with GS1 nutrient codes
  • Ingredients, allergens, and certifications from pack photos
  • Barcodes decoded from the same images

• Developers & integrators

  • A documented REST API with an interactive explorer
  • A webhook when each batch finishes
  • 16 Zapier actions, plus Make, for no-code workflows

• Workspace owners

  • Members invited, removed, or made owner
  • Work types switched on or off for the workspace
  • Credit purchases, invoices, and utilisation charts

• Agencies & multi-brand teams

  • Several workspaces under one sign-in
  • Separate credits, API keys, and members per workspace
  • Switching between workspaces without signing out

• Our support team

  • Email alerts for any step that fails after its retries
  • Credit pricing and monthly credits set per workspace
  • AI token usage tracked for every result

1. 2022

   Foundation

   Workspaces with Google and email sign-in, the first AI product descriptions, batches, and the start of the client API, from October.

2. 2023

   Launch

   Image digitisation and text transforms, credits bought by card, the public API and its documentation, a production deployment pipeline, then nutrition tables and translation.

3. 2024–2025

   Integrations & packaging

   Table digitisation moved to AI, an AI test suite on real packaging, completion webhooks, the Zapier app, Google and GS1 categorisation, and back-of-pack extraction with barcodes.

4. 2026

   Modernised

   A container-based deployment path with health checks, and nutrient codes validated against the full GS1 list.

• Every AI task has its own background queue, so a slow step doesn't hold up the others, and the large batches clients send before Easter and Christmas are worked through in order. Steps that depend on another wait for it, and if a prerequisite fails, the steps after it are flagged rather than run on missing input.
• AI output is checked rather than trusted: categories are chosen from candidates found in the real taxonomy, nutrient codes are validated against the GS1 list, and units and acronyms are normalised.
• Every AI call is stored with its request, raw response, and token count, so any result can be traced and its cost understood.
• Credits are reserved when a batch starts, refunded automatically when a test run fails, and can be refunded when a batch is retried.
• Over 100 automated tests, including AI tests run against 54 real label, table, and packaging images, with every nutrition row checked.
• The AI model for each task is swapped as better ones appear, without changing the API.
• Kept current with regular technology, library, and framework updates, along with ongoing performance and security improvements.

• .NET
• ASP.NET Core
• SQL Server
• Angular
• Python
• Zapier
• Make