AI-Powered
Artist Assessment.

Our client evaluates emerging musical artists to inform real investment decisions — who to back, how to develop them, where to put resources. Each evaluation meant an expert working through 50+ survey responses, scoring an artist against 20 development criteria, and writing a strategic report with a personalised roadmap.

It worked, but it didn't scale. Every assessment burned 4–6 hours of expert time. Different consultants reached different conclusions on similar artists. Capacity capped out at 15–20 a year, and the cost made growth uneconomic. Worst of all, none of the accumulated judgement was captured — no way to learn from what had worked before.

We designed an AI system that does the heavy lifting in 15 minutes while keeping the expert in charge of the decision.

It reads every survey response — the numbers and the open answers — and turns them into a structured, comparable profile across all 20 criteria. It benchmarks each artist against a growing library of past assessments to surface the patterns that have preceded success. And it produces two reports: a full analytical view for the team, and an encouraging, practical roadmap for the artist.

Crucially, it's built to be trusted. Every insight carries a confidence rating. Every AI-generated claim is checked against the source and linked back to it, so a consultant can verify anything in a click. The expert isn't replaced — they're handed a faster, more consistent, fully auditable starting point.

The same team now handles five times the volume, at a fraction of the cost, with far greater consistency — and a system that gets smarter with every assessment it runs.

The work that used to be a bottleneck became a competitive advantage.