The problem
Getting that list right is harder than it sounds.
Bucket List Events books blocks of hotel rooms for marquee events like the Kentucky Derby and the Macy’s Thanksgiving parade. Before any guest arrives, the team has to hand each hotel an exact list: who’s staying, which nights, and what bed type. Guests often check in before they ever meet anyone from Bucket List Events, so when the hotel’s list is wrong, the guest is the one who finds out.
Getting that list right is harder than it sounds, because what was actually sold lives in three places that never line up: the point-of-sale system (the most accurate record), the hotel’s own list, and the team’s operations spreadsheet. Ben from Bucket List Events calls the point of sale “the bible.” For a small event like the Derby, around 25 rooms a night, he can spot-check it in a few minutes. Thanksgiving is the hard one: 200 to 300 guests across several hotels and several lists. “That’s the one that I do by hand, where I go through line by line and check it,” he said.
The solution
A two-step rooming tool that runs inside Claude.
Benali built Bucket List Events a two-step rooming tool that runs inside Claude AI, the assistant the team already works in. First, a consolidator takes the nightly point-of-sale exports and rolls them into one clean master list: one row per room, with each guest’s name, dates, and bed type. That master list, drawn straight from the most accurate source, is what the team now sends to the hotels.
Then comes the audit. When a hotel sends its list back, Claude checks it against the master, and checks the operations spreadsheet against the master too. It matches names even when they’re written differently, puts every date in one format, and flags every mismatch: a wrong check-out, a wrong bed type, a missing guest, an extra room, each with the two values side by side and a note on what to fix.
It’s built to over-flag rather than miss, and it never just says “looks good.” It does the bulk of the work and hands the judgment calls back to a person. To prove it out, the team ran it against a rooming list with errors planted on purpose and confirmed Claude caught exactly those, and nothing spurious.
Time & impact
Every mismatch caught before a single guest arrives.
The old way meant checking hundreds of rooms by hand, line by line, across three lists that were never in the same shape. Now the consolidation is automatic, and the audit becomes a quick scan of only the rows that need attention. The hotels also get a list built from the point of sale instead of the operations spreadsheet, so fewer mismatches exist in the first place.
Here’s the payoff: instead of trusting a by-hand pass across hundreds of rooms, the team gets a complete, checkable list of every mismatch before a single guest arrives. The tool’s built and already in active use, ahead of the team’s biggest event of the year.