For the team at SharkNinja
SharkNinja Feedback Engine: every customer, every market, routed to the team that owns it
A working concept for the Applied AI & Analytics team. Customer feedback from seven markets in six languages is read by a live AI model, every tag is checked against the customer's own words, and each issue is routed through a team directory to the people who can act on it, with spikes and safety reports alerted the same day. It sits next to the voice-of-consumer and field-return work described in your AI transformation postings.
Voice of consumer at scale only works if every tag can be checked
What I noticed
Your Sr. Director, AI Product Development & Product Excellence Transformation posting lists "voice-of-consumer analysis at scale", and your CIO Velia Carboni has said: "We literally live, eat and breathe consumer feedback."
Why it matters
An AI summary that says a seal is failing in the UK gets challenged in the first review meeting. If nobody can show which customers said it, teams stop trusting the tool and go back to reading reviews by hand.
The fix
The AI has to quote the customer's exact words for every tag, and code checks each quote against the original message before anything is shown or emailed. Across 118 messages in six languages, 162 quotes were checked word for word and none were invented. In the demo, hover any tag to see the words behind it; non-English messages keep the original next to the AI's translation.
Evidence travels with every tag
type Evidence = {
quote: string // copied from the ORIGINAL text, any language
supports: "category" | "issue" | "severity" | "safety" | "routing"
status: "exact" | "near" | "unsupported" // decided by code, not the model
}
// unsupported quotes never reach a screen or an emailA spike should reach Quality before it becomes a return
What I noticed
Your AI Engineering Transformation posting asks for "detecting field-return patterns at scale", and Mark Barrocas describes SharkNinja as "growing our business one five-star review at a time."
Why it matters
A seal that starts leaking after two weeks shows up in reviews in week three and in returns weeks later. By then it is a production batch, not a complaint.
The fix
The Feedback Engine groups messages by product and problem and compares the last three days with the earlier daily average. In the demo, a blender seal leak goes from a report every few days to 14 in three days across the UK and Germany, and Quality, Kitchen Engineering and Product Safety get one email with the trend, a cause clearly marked as a hypothesis, and the customers' own words. The AI writes each summary, and any summary containing a number that is not in the data is rejected. Any mention of burning, smoke or sparks, in any of six languages, goes to Product Safety immediately, even if the model misses it.
Your org chart should be the router
What I noticed
International net sales grew 36.6% in Q2 2026, and your AI transformation leaders are building teams of "AI Fellows" embedded in product and quality teams.
Why it matters
The same robot vacuum struggles differently on thick rugs in Indian homes than on American carpet, and the people who can act on that sit in a regional team, not a global inbox.
The fix
Teams load a directory: what each team owns, for which products and regions, and its email. The AI routes against it and code rejects any team that is not in it, so routing follows the org when it changes, with no retraining. In the demo you can edit the directory and watch messages re-route, or type your own complaint in any language. Routing is measured against 61 labeled messages: the right main team 97% of the time, 4 of 4 safety reports caught, and all 7 misses listed with reasons.