So, uh, especially given the volume, the sensitivity, and ambiguity of information that you deal with on a day-to-day basis, I am really curious, um, what that first thing is that comes to mind when we're thinking about data management, especially in the age of AI and, uh, w-where the semantic con-concept comes int-into play in your practice, if at all.

Yeah.

It's a really good question and I guess initially I'd start by saying I see the impact being at two ends of the scale for us, both at the start, so one of the largest challenges we have is when a litigation, an investigation, arbitration, or any form of legal case starts, we really need to understand what data we have, what is available, uh, where does that sit, who has access to it.

So having organizations have a way of managing that data, um, without having to sort of manually ask custodians what they have and believe in their responses, having better data governance and information governance across a, an organization has such huge knock-on effect later down the line.

Uh, the other end of that is now that we have that data, legal teams, case officers, investigators have their own sort of, um, like, purpose and goal with that data.

They need to understand it from a particular lens without necessarily having all of the domain knowledge that the underlying custodians would have of that data and they might work on hundreds of cases, thousands of cases looking at similar different, um, similar case types, wanting to apply lessons learned, uh, knowledge that they've developed over the years or over their career on those different cases, um, but wouldn't necessarily know exactly what terms to search for in this data for this particular, let's say, defendant or investigation.

How, uh, how has that evolved with AI for you in, in, in your space and what, what's working well? What gaps are you seeing when it comes to providing that knowledge and context to your users?

users?Yeah.