Broadsheet deals, captured accurately and alike

1. 01

   Free text, poor accuracy

   Deals were typed into a very basic Windows tool as free text, with nothing to check them, so mistakes and inconsistencies went straight through.

2. 02

   Every retailer is different

   Each retailer lays out its deals, names its products, and runs regional editions its own way, but the data has to come out the same.

3. 03

   Data people rely on

   The deals feed pricing reports for the client's own clients, so a wrong price or product has to be caught before it goes out.

An advert, end to end

1. 1

   Set up

   An advert is created with its retailer, publication, region, and dates. Its pages are uploaded as images or PDFs, put in order, and kept together as one PDF, and it's assigned to a capturer with a priority and due date.

2. 2

   Read with AI

   Users with permission send the advert to the AI service. It finds each deal on the page and reads the products, sizes, prices, and deal type, and the results come back with each deal outlined on the image.

3. 3

   Capture

   The capturer works through each deal: deal type, promotional mechanic, products, pack sizes, and prices. Products the AI read must be reviewed, and known spellings are mapped to the right brand and product.

4. 4

   Check

   Required fields are enforced, prices are compared with the product's reference price and its recent average in the same currency, and category rules for the retailer type apply. New products wait in staging.

5. 5

   Complete

   The capturer marks the advert done, and a user with completion rights completes it. Matching deals in other regions' editions can be copied from the completed original for review.

6. 6

   Report

   Completed deals are synced to the reporting database at 06:00, 12:00, and 17:30, with a check at 20:00 that every eligible deal is there. From there they feed the client-facing reports.

• AI deal extraction

  A custom-trained vision model finds the deals on each broadsheet page, and AI reads their products, sizes, prices, and promotions. Results are drawn on the page so the capturer can see where each deal came from.

• Structured deal capture

  Every deal is captured as straight, bulk, combo, or multibuy, with one of 8 promotional mechanics and a product configuration such as single, pre-pack, banded, or mixed. Capturers can flag a query or a missing brand.

• Price checks

  Each price is compared with the product's reference price, with a warning from 15% and a block from 20% by default, and with its 3-month average in the same currency. A capturer can override the average check when a price really has changed.

• Brand & product mapping

  New products are held in staging until they're approved. Spellings from reviewed AI results are learned as aliases of the right brand and product, and a blacklist keeps wrong matches from coming back.

• One standard structure

  Retailers with groups and types, regions with their currencies, publications, brands and brand owners, and a 4-level category tree, so every deal is described the same way.

• Regional editions

  When a retailer runs the same deal in several regions' broadsheets, matching deals are recognised by their content and copied from the completed original, ready for a capturer to confirm.

• Reporting sync

  Completed deals, with a link to the advert's PDF, are synced to the reporting database three times a day. An evening check confirms every eligible deal arrived and repairs any gaps.

• Workload & progress

  A dashboard of capture figures, top capturers, each capturer's workload, and incomplete adverts, an hourly capture report on weekdays, and a morning email listing older adverts still incomplete.

• Capturers

  • Adverts assigned with a priority and due date
  • AI-read deals to review instead of typing
  • A warning before a doubtful price is saved

• Capture managers

  • Each capturer's workload on a dashboard
  • An hourly capture report on weekdays
  • The final say on completing each advert

• Administrators

  • Retailers, regions, publications, and categories in one place
  • Brand and product mappings they can correct
  • Permissions set section by section

• Report users

  • Deals in one shape across retailers and regions
  • Data refreshed three times a day
  • A link back to the advert each deal came from

1. 2020

   Rewrite

   Adverts, deals, products, reference prices, and sign-in with company accounts, built from June and set up for production by October.

2. 2021–2023

   Reporting

   Reference price rework and a dedicated reporting database synced through the day.

3. 2024

   AI extraction

   AI deal extraction tested against captured adverts, then connected to capture through a separate service.

4. 2025–2026

   Fewer repeats

   Regional deal copying, learned brand and product mappings, and one PDF per advert.

• AI as an assist, not the last word: products it reads have to be reviewed before a deal can be completed, and sending adverts to it is a permission.
• Two-step completion and an audit trail: a capturer finishes an advert, and a separate permission completes it.
• Regional copies matched by a hash of the deal's content, only from completed deals for the same retailer within 30 days.
• A sync built for volume: deals are sent in chunks that never split an advert, source data is loaded in batches, and an evening reconciliation repairs any gaps.
• Large images and PDFs uploaded in parts, and each advert's PDF rebuilt whenever its pages are added, removed, or reordered.
• 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
• Computer vision
• AI
• GitHub Actions