You claim customer feedback loops but have no customers. What is actually built, and what closes the loop when users finally arrive?

The full executive answer

Every loop is shipped code waiting for signal, and I can demo each one. In-product NPS: a scoring module classifies zero-to-ten ratings into promoters, passives and detractors, computes the score, and bands it — with verbatim comments captured alongside. AI-output feedback: users rate AI-generated analyses, which is the loop that matters most in a governed-AI product, because it feeds our evaluation harness — the test suite that scores prompt quality in CI without live AI calls. Behavioural telemetry: the account-health engine tracks activity recency, seat adoption, feature breadth and quota pressure per org, and snapshots it over time, so we see what users do, not just what they say.

Closing the loop is defined, not aspirational: NPS detractor verbatims and feature-locked events flow into the RICE inputs I described; AI-quality feedback becomes new eval cases so a complaint about one bad analysis permanently hardens the harness; and shipped responses land in the public changelog, which is the visible receipt that feedback moved the product. Pre-launch, the loop runs on proxy users — my own advisory conversations and demo audiences — which I will not dress up as customer data.

The framing I hold myself to is continuous discovery: the instruments exist so that from customer one, the feedback cadence is weekly and structural rather than an annual survey ritual. The honest gap is N — the machinery is real, the sample is zero, and I would rather show you an empty, well-built instrument than a full, fake one.