Retail.

Retailers see clear unit-economics from generative AI in three places: search, product data, and post-purchase support. Almost everywhere else, the ROI math gets fuzzy fast. In practice, the first deployments cluster around search & re-ranking, structured extraction, customer-care deflection, internal copilots: areas where evaluation criteria are objective, data is already in the system, and an evaluation harness can measure quality continuously.

Regulators-of-note for retail engagements typically include ACCC, CMA, FTC, PDPA. The specific regulators that bind a given engagement depend on jurisdiction and the data classes in scope, so we map this explicitly during discovery rather than assume a global posture.

We do not deploy generative pricing or markdown agents without explicit human approval gates; price-action automation stays out of scope. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in this industry does not justify it.

Our retail engagements typically sit between Chief Digital Officer, Head of E-commerce, Director of Customer Operations and equivalent senior operators. The decision-maker varies by organisation, but the common thread is a leader accountable for both delivery and downside.

In retail, generative AI shifts from buzzword to bottom line via search re-ranking, product-attribute extraction, and post-purchase support deflection. The same techniques look superficially similar across industries, but the data shapes, evaluation criteria, and adverse-event posture differ enough that copy-pasting an engagement from another industry usually produces a system that fails the first audit it sees.

We spend two weeks with your leadership and operators, rank the use cases that are worth doing, and come back with a clear plan and a clear price. We never start with a six-month strategy exercise. We go after the smallest piece that produces real value first.