Kesava (Kesav/Keshav) Dinakaran
speaker
203 appearances
1 recordings
1 series
first heard Jul 2026
last heard 30 Jul
Kesava (Kesav/Keshav) Dinakaran’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
Appearances
you know, this state of healthcare.
And as I started to go deeper and deeper and deeper, I realized no better application of this technology of now, you know, what we call LLMs back then, basically baseline automation technology in some of the work that, you know, is happening across America and the rest of the world.
And so that's sort of, you know, how the healthcare being kind of came together and the creation of Lumini happened in the first place.
Lumini helps large health systems move their operational overload and operational manual workflows to computers and AI.
That's the most simple sort of definition.
Maybe the more tactical version is we believe
the 30% of spend, 30 to 40% of spend that is going into these like incredibly manual paper pushing, very, very operationally intensive jobs can be finally moved to AI systems so that those people can actually go do the work that they were meant to do
As you know, we work with your alma mater in Cleveland Clinic, and it might be good to just kind of describe and talk about how this shows up in the real world, which is our belief is historically, if you think about the software paradigm, they were built to solve very verticalized, very specific point problems.
And it worked for the previous era of software.
But what's happened since then is essentially this creation of hundreds, if not thousands of point solutions that have been procured by these health systems, that the admin overload of actually managing them is worse than the actual ROI and value that they're individually creating.
And the reality is like,
That was the case because of the software paradigm that we were in prior.
With the advent of these models and the advent of AI at the scale we're in today, we have this sort of very special opportunity, really, which is to create one fundamental infrastructure
where you can basically build a set of agents or workflow automations on that one infrastructure that can very rapidly scale across a variety of different workflows and share context and essentially drive large scale automation in a way that's never been done or been possible before in the first place.
If you think about a real-world use case today, the Cleveland Clinic, for example, receives millions of faxes on a yearly basis, where 2026 and faxes is really the unfortunate reality of how communication happens today.
And what Lumina is essentially doing for them, for example, is we've become basically the first line of defense for those type of documents that are coming in.
And our agents, what they do are essentially go through these documents, extract, first index them, understand them, triage them, extract the information from them, be able to actually do risk stratification on them, meaning if these are incredibly urgent, high critical patients or documents that need to be processed immediately, do that, and then kick off essentially the downstream workflows.
And so in many ways, the work that
should be done or were done by these large operational teams that were being outsourced in many ways are now basically done through these AI systems so that the processing of patients is just significantly faster and much more relevant today than ever before.
And so that's sort of the starting point in many ways of what is an example of one use case.
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