Photo AI. An archive that tags itself.
IGD's researchers photograph stores, shelves and products around the world, and the archive is one of the organisation's most valuable assets. It was also one of the slowest to use. Photo AI is a rapid AI pipeline that tags and normalises every image as it arrives, so the archive can be searched, compared and, next, read for trends.
A photo archive nobody could search
Years of store and shelf photography, shot on cameras by researchers in the field, loaded onto laptops and pushed up in batches. Inconsistent framing, lighting and naming meant the archive was only useful to the person who took the pictures. The frontier report had tracked vision-language models crossing from demo to deployable for retail imagery; Photo AI was the concept that followed.
A pipeline that normalises and tags on ingest
Every image is normalised as it lands: orientation, exposure, cropping and naming brought to a common standard. Then it's tagged by a vision model against a taxonomy IGD's researchers already use, so retailer, category, fixture type and promotional mechanics are searchable across the whole archive rather than remembered by whoever was in the store. Basic by design in the first stage: reliable tags first, clever analysis second.
Multi-model, tested against the archive first
Candidate vision-language models were benchmarked in the lab against a hand-labelled slice of IGD's own images before anything was built, and the pipeline uses more than one: a fast model for normalisation and coarse tags, a stronger one where confidence is low. Storage and processing run on Edge alongside igd.com, so the archive never leaves the platform that serves it.
The archive became a dataset
Researchers search by what's in the picture, not by who took it or when. New photography is usable the moment it's uploaded. And because every image now carries structured tags, the archive is for the first time a dataset that can be analysed over time, which is what the second stage is for.
Capture on a phone, trends across the archive
Two moves, both scoped in IGD's quarterly report. First, replace camera-to-laptop-to-upload with a mobile capture app on a high-end phone that streams straight into the pipeline, so a shelf is tagged before the researcher leaves the aisle. Second, read the archive for trends: share of shelf, promotional intensity and range changes across retailers and quarters. Run by Edge Expert Services; extended by Labs when IGD says go.
A rapid build with a second act designed in.
Photo AI was deliberately scoped small: get tagging reliable and useful, then earn the right to do trend analysis. It's a good example of a Labs concept that starts as a fortnight's work and grows into a roadmap.
- Client
- IGD, a customer of Edge since 2024. Edge hosts and runs igd.com.
- Sector
- Food & grocery · Membership and research body
- Status
- Live, stage one. Stage two scoped.
- Labs' role
- Frontier report → concept and spec → rapid build → handover
- Now run by
- Edge Expert Services
- Stack
- Multi-model vision pipeline · Taxonomy-constrained tagging · Edge Storage and Compute
- Related
- AskIGD · The IGD sample report
Data you already own, made useful.
Most organisations are sitting on an archive like this. If yours is images, documents or recordings that only their author can find, tell us about it.
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