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Every time new attendee names land in your sheet, Sai searches LinkedIn for each person, writes the profile URL, title, and company into the row, and labels how confident the match is.
An attendee export gives you a name, maybe a company, and nothing you can act on. The names are the whole value of the event and they arrive in the least useful shape possible: two hundred rows that each need a LinkedIn search, a glance at the profile, and a paste back into the right cell. It is an hour of work per event, it is identical every time, and it is the reason most attendee lists are still sitting in a Downloads folder.
Sai reads the names in your sheet, searches LinkedIn for each one using the company, email domain, or title already in the row as context, and writes the profile URL back next to the name along with the person's current title, company, and location. It runs whenever new names arrive, so a list that fills up over three weeks of registrations is enriched continuously rather than in one panic before the event.
A wrong profile URL is worse than an empty cell, because it sends real outreach to the wrong person. Every row therefore carries a confidence label, and ambiguity is surfaced instead of resolved by guessing.
Six columns written next to the row that already exists: LinkedIn URL, Current Title, Current Company, Location, Match Confidence, Notes. Your original export columns are untouched, so the sheet can still be reconciled against the registration platform, and Ambiguous rows carry their candidate URLs in Notes so a human can settle them in seconds.
Only rows with an empty LinkedIn URL are processed, so re-running after each registration batch costs nothing and never overwrites a URL you corrected by hand. Rows previously marked Not found are retried, which matters because people create or make public a profile between registration and the event itself.
An enriched attendee list is the input to the rest of the pipeline. Feed it into the same sheet your weekly prospect list appends to, then run your weighted lead scoring rubric so the two hundred names arrive ranked rather than alphabetical. Use recurring market research to decide which events are worth attending again, and a scheduled analysis of the sheet to compare match rates across events. Respect LinkedIn's user agreement — this reads public profile information as a signed-in person would, and stores it in your own sheet.