RosterGuard Productization

Context

Vermillio is an AI content protection and licensing company backed by Sony Music. Based on publicly available information, I hypothesized that Vermillio likely had internal detection capabilities that could be productized for enterprise clients (labels, distributors) who need visibility into unauthorized AI use of their artist rosters. I called this concept "RosterGuard."

I wrote this strategy brief on my own initiative during the interview process to demonstrate how I'd approach a 0-to-1 productization opportunity. The scenario, product name, and specific details are my own construction, not based on any internal knowledge of Vermillio's tooling or client relationships.

Problem

The hypothetical scenario: enterprise clients need real-time visibility into what's happening to their artist rosters, plus a documented record they can show artists and attorneys. Without a self-serve product, this kind of information typically flows through manual rep check-ins, which doesn't scale and leaves no audit trail.

The strategic premise: if Vermillio has detection capabilities internally, there's likely recurring revenue being left on the table. The hard technology (detection, crawling, scoring) would already exist. What's missing is the product wrapper that lets clients access it safely on their own.

Approach

• Framed the strategic opportunity. Vermillio's edge is in detection, not operations. The strategy I proposed: automate the ops layer so that detection advantage compounds. Near-term, grow client count without growing headcount. Medium-term, build switching costs as roster fingerprints and audit histories accumulate. Long-term, reposition from service vendor to infrastructure.
• Scoped a phased build. Phase 1: discovery and architecture assessment (2-4 dev-weeks). Phase 2: build and closed pilot with early adopter clients. Phase 3: general release, contingent on pilot performance. Each phase had explicit gates and go/no-go criteria.
• Defined what not to build. Client-initiated takedowns without Vermillio approval (liability risk), API access before multi-tenancy hardened (security risk), and net-new analytics nobody had asked for (feature bloat). The non-goals were as important as the scope.
• Built the revenue model. Per-seat license ($50/mo) plus per-monitored-talent fee ($8-10/artist/mo). At assumed adoption rates: $30-35K ARR per client, scaling to $600-700K at 20 clients. Engineering cost is fixed; revenue scales.
• Named the risks explicitly. Cross-client data exposure as highest priority (label trade secrets). Multi-tenancy complexity as biggest timeline variable. Client willingness to pay vs. loving free access. Platform dependency for crawling infrastructure.

Key Decisions in the Brief

Outcome

This was a self-initiated deliverable during the interview process. The brief structured the productization opportunity, revenue model, phased approach, and risk management in a format ready for a stakeholder alignment meeting. It demonstrates how I approach ambiguous 0-to-1 product decisions: start with the strategic frame, scope tightly, define what you won't build, name the risks, and ask for the smallest commitment that validates the next step.

What I'd do differently

The obvious limitation is that this was built without access to Vermillio's actual internal data, tooling, or client relationships. With real access, I'd validate every assumption: Does the detection capability exist in a productizable form? What's the actual multi-tenancy complexity? Do enterprise clients have willingness to pay, or do they expect this bundled? The brief is designed to be stress-tested against reality, not to be taken as-is.