AI Engineering for Retail

Retail computer vision splits into two engineering surfaces that share nothing operationally: shelf-execution systems that read the physical store, and product-discovery systems that match a shopper's image to the catalogue. Both fail the same way — strong eval scores that drift on a packaging change or a catalogue that doubled overnight, and a cost line that compounds at scale. We run each as its own engagement scoped to your problem.

Start a conversation Tell us the store

Where the Engineering Bottleneck Lives

Two buying triggers recur. On the shelf side, a stock-out or planogram deployment hits a packaging variant, store format, or compliance question the original eval set never covered — and the slice-level regression never surfaces in monitoring, usually while the team is trying to reuse the cameras already in the store.

On the discovery side, a visual-search pipeline runs too expensive per query at catalogue scale, the product-image index is costly to keep fresh, and a conversion-lift claim needs to be measured against noise rather than asserted.

Shelf Execution & Stock-Out Detection

Headline accuracy on the original eval set looks fine, then a packaging redesign, a new store format, or a lighting change pulls slice-level performance off-target without the regression surfacing in monitoring. We build the eval harness with the slice cuts operations actually care about — by store format, product category, and lighting condition — gate release until those cuts are within tolerance, and hand over a runbook the store team can rerun.

Lands in the Production AI Monitoring Harness — 4–10 weeks, milestone or fixed-price.

Visual Search & Product Discovery

Visual search — image in, product match out — compounds on cost like any catalogue-scale inference workload: a small per-query win is real margin at production volume, and keeping the product-image index fresh is an ongoing cost, not a one-off. We profile the pipeline — model choice, embedding and index strategy, GPU kernel paths, batching — and move the per-query cost line on the catalogue you actually run, with conversion lift measured against noise rather than asserted.

Lands in the Inference Cost-Cut Pack — 4–8 weeks, milestone or fixed-price.

How We Work With Retail Teams

Each pack has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run — the eval harness, the re-run script, the slice-level monitoring dashboard. We work alongside your store-operations, merchandising, and data functions; we do not substitute for them.

Heading into a shelf-execution release gate, a stock-out monitoring rollout, or a visual-search cost review? The named pack page is the entry point — or contact us and we will route you to the right one.

Start a conversation Tell us the store