Patrick O'Shaughnessy

You just created like a captive reinforcement learning process within an organization.

Is that like the simplified version?

How fast does the spread happen?

If I'm a customer and I've got the 500 person customer service call center or whatever, I'm actually curious.

I don't know what the volumes are, like how much call volume or interaction volume a center like that handles for a given company.

But if I give you 5% of my workload and I'm satisfied with AI's performance, like it performs well and there's not lots of problems, how fast are people willing to go from 5% to 10% to 15% to 20%?

What goes most wrong when something bad happens?

I'm sure this is happening less and less as the product's gotten better.

But even in the early days, what sort of thing would go wrong in one of the customer to AI interactions?

I think most people are still focused on things that could go wrong because it's non-deterministic.

What about the total other end of the spectrum?

What have been the things that have gone way more right than you expected?

Where has the potential of agents outperformed your expectation in terms of what they can handle or what they can do?

Where do you think that can go?

How good can the experience get in ways that it's not yet that good with subsequent evolution of your product, but also of the underlying capabilities of the model?

At the risk of getting too technical, why is voice-to-voice interesting and worth pursuing versus just always going back to text and being able to manage it that way?

So give us a sense today, if you add up all the interactions, some idea of how long they are, what type they are, voice versus text versus some other modality.

What is the entire corpus of interactions between a Decagon agent and a customer look like today?

What could go right for the company based on the data they're gathering from these interactions that they probably have been doing nothing with historically?

Like what new things can they do for their customer because of on a one-to-one basis, like they're just learning more about a person.