Gregory Mostyn
speaker
336 appearances
1 recordings
1 series
first heard Jun 2026
last heard 29 Jun
Gregory Mostyn’s voice in public audio — every appearance, attributed to the second.
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Appearances
What we do is we start with the fact, i.e.
what's actually happened and why does it matter?
And then we build up our scale based on that.
So kind of a totally different data model and re-architecting it from an eDiscovery perspective, you know, requires a complete overhaul and a reinvention of that data model, where
scale, but as I say, we integrate with eDiscovery platforms and it's a slightly different use case.
It's not sort of the paralegal led first level review.
It's like the kind of barrister
a sort of trial attorney who's saying, how do we then use that from a smaller universe of documents?
But why does it matter?
And how is that going to help me drive forward my case?
And then with generalist legal AI, I think it's really about the... Again, it's about that kind of...
distilling the documents into these facts, no matter how complex and messy the different the data set is.
So it could be like, I'll give you an example.
Generous legal AI, it has a bias towards more structured information, tends to be trained on corporate databases, you know, like it's very pro forma, it's very kind of rote transactional work.
You know, the documents are sort of neatly laid out and it's kind of a lot clearer.
You know, there's only so much variance and it's usually like a few sentences that are changed.
Whereas litigation data sets could be like a WhatsApp message, medical record, handwritten doctor's note, an image, a sort of scrap of paper, a marginalia.
whatever it is and so you need to do this pre-processing first to get the best insights and that's what you're looking for in the kind of complex theatre of litigation like in very concrete terms the sort of vault and tabular review features they'll sort of summarise a document as a single row in that platform and the document could be 10,000 pages long like it could be a whole trial bundle and the assistant will also run out of the sort of context to answer those questions so it's all about that that kind of
pipeline, we put the documents through to rationalize and elucidate the datasets into the facts and then use that as the unit of analysis.
And that is what helps you find the
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