Measured the Day-0 baseline: what AI knew before the launch.

The Product Launch agent ran 4 audits on the ITK catalog and surfaced 927 action items across the line. The baseline was built first: which AI engines knew the line existed, which mistook ITK for an unrelated SKU, which had zero awareness at all.

Simulated the launch arc: readiness scored before any outreach went out.

For each SKU the OS modelled three scenarios: (a) launch with current asset state, (b) launch after creator/publication seeding, (c) launch after seeding + retailer feed sync. The OS ranked which SKUs needed the most lift, in what order.

Shipped the launch ops queue: outreach, recency, RSS, the works.

Five launch-tagged action items led the queue. The flagship: "Launch influencer and beauty publication outreach for ITK Deep Moisture Rich Cream" (P1, SOURCE_OUTREACH). Followed by P2 "Update ITK's online presence with 2024–2026 product launches and brand milestones": the recency update that lets AI find the launch at all.

• P1: Launch influencer + beauty-publication outreach (SOURCE_OUTREACH)
• P2: RECENCY_UPDATE for the brand’s launch history
• 60+ supporting recommendations across the 6 SKUs

Compounded: the launch playbook is now a templated workflow.

ITK was the first launch the OS handled end-to-end without a `launch_workflows` table. The discovered playbook (baseline audit → readiness modelling → outreach queue → recency update) is now packaged as a single workflow any merchant can trigger on a new SKU.