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.

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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.

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