Music, data, and the automations in between.

Behind the workflow

• Prompt contract — five fixed sections (Key Signals, Why It Matters, Connections, Watchlist, Priority), instructed to work only from the supplied evidence and separate fact from interpretation.
• Output format — the model writes valid HTML directly, so Gmail sends it as-is with no manual formatting step.

Validated

Tested end to end with three real signal types in one run — a release, a tour, a collaboration — correctly summarised, connected and prioritised.

Key learning

Aggregation is what makes analysis possible. Without it, the AI sees one row at a time; with it, the model receives one intelligence packet and can reason about the set — the same distinction that later shaped the Opportunity Radar's scoring logic.

Behind the workflow

• Output contract — artist, opportunity_score, level, reason, recommended_action, returned as JSON and parsed into fields Make can route on.
• Contextual intelligence, by design — the model is allowed to bring background knowledge to the score rather than treating the artist as a neutral identifier — closer to how real industry judgement works. The trade-off is explicit: this is an AI-assisted assessment, not a purely mathematical index.

Validated

All three priority routes tested with real data — a High-scoring alert delivered by email, a Medium signal added to the watchlist, a Low signal recorded without action.

Debugging notes

• An unexpected High result traced back to a downstream module reading a stale sheet — a reminder to inspect module output before touching logic.
• Proving the Low route required changing exactly one variable (artist) while holding the signal constant — isolating cause from coincidence.

Behind the workflow

• Reading the API's own feedback — the first request came back as a Bad Request; correcting the query syntax produced a clean 200 — a reminder that an error response is information, not just an obstacle.
• Filter vs. AI — where the selection rule is objective (score equals 100), a plain filter is the right tool. Contextual judgement across several fields is where an AI layer earns its place — this integration deliberately uses the former.

Validated

Tested with Radiohead: correct top match returned — MBID, type Group, country GB, relevance score 100.

Key learning

Widening the result limit during testing surfaced ranked alternatives, confirming the API returns candidates to evaluate, not a single guaranteed answer.