I kept showing up to events and wasting time.
You know the drill. You get a guest list. A PDF, a Luma page, maybe a spreadsheet if you're lucky. Names and companies. That's it. No context. No signal. You walk into a room of 200 people and you're basically guessing who to talk to.
I got tired of that. So I built WhoIsAt.
What it did
You'd drop in a list of attendee names. Four AI agents would run in sequence: find the person online, pull their company context, build a profile, then score them against your goals. You'd get back a ranked list. Who to prioritize, who to skip, and a short reasoning for each.
It worked surprisingly well. The enrichment was solid, the scoring made sense, and I could walk into an event actually knowing who was in the room.
I even started experimenting with conversational queries on top. Things like "who here is building AI infra and raised a Seed?" and it would filter and answer from the enriched data.
Why I killed it
Honestly? The market didn't care enough.
The people who'd benefit most (founders, BDs, investors hitting conference circuits) are used to winging it. The pain is real but it's not acute enough for most people to pay for a tool. And the ones who do care already have assistants or internal processes.
I also realized I was solving a problem that only happens a few times a year for most people. Hard to build recurring revenue around something seasonal.
The tech was fun to build but the business wasn't there. So I shelved it.
What I took from it
A lot, actually.
This was one of my first real multi-agent builds. Chaining four agents in sequence, handling failures gracefully, making the scoring actually useful. That taught me more about agentic architecture than any tutorial could.
It also taught me to validate demand before falling in love with a solution. I built WhoIsAt because I had the problem. Turns out having the problem yourself doesn't mean there's a market.
The agents, the patterns, the enrichment pipeline. All of that thinking carried forward into what I'm building next. Different problem, better market, same agentic mindset.
No regrets.
- D