Shed Happens: photo-score shed antlers.
A mobile app for antler collectors: photograph a shed antler dropped by a deer, elk or moose and computer vision measures it — beam length, point widths, root spread — then maps those measurements to a standard scoring card. Each scored find lands in the collector's profile, where a community of pickers share and compare what they've found.
Anonymised case study. Project names are aliases used to protect client confidentiality. The case study covers the project’s challenge, approach, technology and outcome.
The challenge.
- Scoring a shed antler meant a tape measure, a paper card and manual arithmetic — slow, error-prone and hard to compare across a collection.
- The client wanted a collector to point their phone at a find and get a measured, scored result on the spot, without learning the measuring system by hand.
- Collectors also wanted somewhere to show finds off — a profile and a community feed to compare scores with other pickers.
Our approach.
- Built a React Native app so collectors can photograph a find and score it in the field on either iOS or Android.
- Used computer vision to derive the antler's measurements from the photo — beam length, point counts and widths, root spread — and mapped them onto the standard scoring card so the app returns a score, not just a picture.
- Gave every collector a profile and catalogue, backed by a Django and Django REST Framework API, where each scored find is stored with its measurements and photo.
- Added a community layer so pickers can publish finds, browse others' catalogues and compare scores within the community.
The outcome.
- A collector photographs a find and gets a measured, scored result instead of working a paper card by hand.
- Every find is stored with its measurements, score and photo in the collector's catalogue for later comparison.
- Pickers share finds to a community feed and compare scores, turning a solitary hobby into a social one.
Related retail & commerce work.
Anonymised case studyCommerceFrontir: emerging-markets research.
Research clients access representative panels in markets that were previously unreachable.
React · Redux · PHP · Slim
Anonymised case studyCommerceOrdermint: E-commerce platform.
A web app for online retail, built with Django, AWS, Python, jQuery and Bootstrap. Catalogue, inventory, checkout and fulfilment run off one backend, so what shoppers see is always what the business can actually sell.
Django · AWS · Python · jQuery
Anonymised case studyCommerceServora: Custom application.
A web app for a focused workflow, built with Vue, Django REST Framework and PostgreSQL. The core workflow is modelled properly once, so data stays consistent and the product is straightforward to extend.
Vue · Django REST Framework · PostgreSQL
Where AI fits in retail & commerce.
Search that understands what shoppers mean, product copy and imagery generated against your brand rules, and support assistants that check order status before they answer.
Explore AI developmentClient feedback on our work, separate from the anonymised project described above.
One of their strengths is quickly building out a multi-disciplinary team to work on the development and delivery of features with different technical requirements. Their support has allowed us to accelerate our development work without building an internal team. They have also worked seamlessly with other third parties both during the acquisition of Dragonfly and development of our mobile app.
Questions about this build.
Still wondering something? Ask an engineer
You photograph a shed antler and computer vision measures it — beam length, point widths, root spread — then scores it against a standard scoring card. Each scored find is saved to your profile, and you can share it with a community of other collectors.
The app derives the measurements a scoring card needs from the photo rather than asking the collector to measure by hand, then maps those measurements onto the standard card to produce a score.
A React Native app on iOS and Android with a computer-vision measurement flow, backed by a Django and Django REST Framework API for profiles, catalogues and the community feed.
Discuss your software project.
Describe the workflow, existing systems and the change you need. Use the enquiry form to discuss scope and next steps.