Olivia Moore

And also when there's,

this high complexity when the decisions are contextualized and the SOPs are not super clear.

I think that's the bigger point where sometimes the enterprise doesn't really know themselves.

People don't know what they know.

You can ask them,

what they're doing.

And it's like, well, I'm doing this, but they really don't know the specificity of what they're doing.

So it's actually through doing this execution of work that we're learning a lot about how these companies operate.

So when the SOPs are not clear and it's like super communication driven, I think that's where we shine.

Yeah, my 11 is great.

We actually use them for a long time and they're great, of course.

I guess to the point before, I was always focusing on the limiting factor and seeing what do we need to do to solve the current problems of the market.

I guess we started very soon realizing how...

there was a problem in like turn taking detection like end of turn is probably the biggest problem in voice ai and we realized that very early on because everyone was focusing on like making the latency lower and making the voices more realistic and that's fine but i don't really think that's the bottleneck right now to to deployment of these agents not even the intelligence like model capability is

high enough.

Like we're using models in certain uses that were released like two years ago.

Right.

Like sure, like everyone is like pushing the frontier and increasing context windows and making more reasoning budget.

Exactly.

Like everyone is waiting for someone to release like a 10 trillion talking context window to like do whatever.