Case study

Sneaker Visual Retrieval System

AlwaysLegit needed a way for their team at expos to instantly identify shoes from photos and match them against a massive 150K+ SKU database, often in challenging real-world lighting and backgrounds.
  • Client: AlwaysLegit
  • Industry: Retail & E-commerce
  • AI capability: Computer Vision
Visit AlwaysLegit (opens in a new tab)
Blue sneaker with yellow laces and a handwritten size and price tag, photographed on a table at a sneaker expo
SKUs searchable from a single photo
150K+
Retrieval time per query
<1s

The challenge

150,000 SKUs, one photo, and an expo floor in the background

The primary challenge was the scale and speed required. Matching a single photo against 150,000 products in under a second is computationally intensive. Furthermore, photos taken at busy expos often have cluttered backgrounds, varying lighting, and multiple angles, making traditional image matching unreliable. The system needed to be robust enough to handle these real-world variations while maintaining extreme accuracy to ensure the correct SKU was identified.

What we built

Visual search that answers in under a second

Find the shoe, turn it into an embedding, search the catalogue.

  1. Stage 1

    Isolate the sneaker in the photo

    A sneaker-specific detection model finds the shoe and crops away the expo background: people, booths and surrounding products.
    Clean sneaker crop
  2. Stage 2

    Turn the crop into an embedding

    Deep learning feature extraction produces a high-dimensional embedding of the shoe's structure and colour distribution: panel layout, shape, branding patterns and colour signature.
    Shape and colour embedding
  3. Stage 3

    Search the SKU catalogue

    The embedding is matched against a vector database holding representations of more than 150,000 sneaker SKUs, using approximate nearest neighbour search built for that scale.
    Ranked candidate SKUsVector databaseApproximate nearest neighbour
  4. Stage 4

    Return the product record

    The closest match comes back with its product details, so the team on the stand can answer in seconds instead of searching a catalogue by hand.
    Matched SKU in under a second
  • The visual search dashboard. For this query, detection took 0.04 s and the database search 0.58 s.

The impact

Anyone on the stand can identify a shoe from a photo

  • 150K+ SKUs searchable from one photo
  • Under a second per query
  • Works on cluttered expo photos

The activation transformed how AlwaysLegit interacted with enthusiasts at events. Attendees could take a photo of any sneaker, and the system would instantly provide the exact product details, pricing, and availability. This not only provided a 'wow' factor but also significantly improved the efficiency of their on-site team, leading to higher engagement and a larger volume of SKU lookups than previously possible with manual search.

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