AI Readiness Assessment for E-commerce

Sometimes the recommendation is to buy, not build. We say so, including when it means less work for us.

In e-commerce, every change must be justified by a controlled experiment against revenue. That single fact reshapes how ai readiness assessment has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is product recommendations, usually integrated against logistics aggregators. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Multi-model by default, so a provider outage is a routing decision rather than an incident. We hand over with runbooks, tests and a team that knows how it works, not a dependency.