WhatsApp Audio 2026-05-04 at 11.57.48 PM

Speakers

• Speaker 1 0:00
• Speaker 2 0:44
• Speaker 3 0:29
• Speaker 4 28:52
• Speaker 5 38:28

1. 0:00

   Chapter 1: Any experience in that space with data engineering? 299s · Speaker 1

   Any experience in that space with data engineering? Yeah, 100%. We've also been doing, with all of the advancements on AI and Agents, we've been doing a lot of Agented ETL, like just maybe painful data engineering work, which is representat…

2. 5:01

   Chapter 2: that they currently need to get to where they want to get to in the future. 104s · Speaker 1

   that they currently need to get to where they want to get to in the future. Unlocking skillset data is huge, huge unlock for HR. but also what are the skills that they have because you know they've been in this job for five years let's say …

3. 7:19

   Chapter 3: So let's jump in, go forward. 159s · Speaker 2

   So let's jump in, go forward. I don't think it is unique to home people, but it's happening across all the organizations at this point. There are obviously a lot of investment at work to put towards fixing the problem. you know, the last pl…

4. 10:00

   Chapter 4: So we were using the data and data and computation as remote models and we do executives built on top of everything being all the compliance angles that are specifically taking care of the AI and the HR and then finally the organization's essence which is the culture and retain belonging so the solution that really works for home people might not work somewhere else where the culture is built. 72s · Speaker 1

   So we were using the data and data and computation as remote models and we do executives built on top of everything being all the compliance angles that are specifically taking care of the AI and the HR and then finally the organization's e…

5. 11:16

   Chapter 5: Got it. So this is like a command center what we are calling it and this is specifically built to demonstrate the idea of there are a lot of folks in the organization, they are doing a lot of things but if I look at from the organizational lens, are the eight or ten pillars that needs to move from an HR ecosystem, are they moving? 219s · Speaker 2

   Got it. So this is like a command center what we are calling it and this is specifically built to demonstrate the idea of there are a lot of folks in the organization, they are doing a lot of things but if I look at from the organizational …

6. 15:00

   Chapter 6: I've zoomed out of my screen a bit too much. 168s · Speaker 1

   I've zoomed out of my screen a bit too much. Is it any better now? So the idea is, this is not limited to only emails, themes and calendars. I'll just zoom into emails. So I got an email related to financial merit or legal or HR Ops and the…

7. 17:50

   Chapter 7: Can you talk a little bit? 129s · Speaker 3

   Can you talk a little bit? Did y'all build the algorithm around system analysis? Or is that something that is fueled by a system? Or is it like a game sort of discipline maybe? Is that a Christian predictor? How is that? will cover somethin…

8. 20:00

   Chapter 8: rather than doing majority of the operational work. 64s · Speaker 1

   rather than doing majority of the operational work. So yeah, that's one of those instances. media and product and I used to see which we let people like things out online, also offer services. So from a group of text, I didn't know what thi…

9. 21:17

   Chapter 9: And the data that you feed in would be . 222s · Speaker 3

   And the data that you feed in would be . So, workday, external survey, site, internal survey information. If you have external information, show strength of information, like our LinkedIn, start sharing that data. So, they can all be protec…

10. 25:00

    Chapter 10: system i should be able to see like 45 kpis that is like off track that's going off track and let's say if you see v2 shortlist is one of them or maybe like hard to fill is something that that is going like off track so you can basically click there what is the root cause that is driving so we have we have a hypothesis agent that does all the magic that that is able to navigate through the data run like all the analysis or the hypothesis statistical tests and is able to diagnose and tell you and this business impact is something what we have sort of done like using basic guesstimates and things like for our customers they were very interested to see like let's say if we are not filling a particular role in a particular time then how much business is it like on a dollar level this driving so you can calculate that and to fill if you want to like take it maybe take the KPI what does it take you to go ahead from there and then we did not want to create a workflow management system here so here once the user see they acknowledge the system that's saying oh this is a great recommendation and they can track that kpi going ahead that if that kpi is reducing or increasing or what's happening with that and then you can do like bunch of comparison like year-on-year comparison month-on-one comparison and then basically you can download a report or a presentation out which you can share it with your leadership so So that's the whole idea of this command center view because every person has so many different responsibilities to take care of. 213s · Speaker 2

    system i should be able to see like 45 kpis that is like off track that's going off track and let's say if you see v2 shortlist is one of them or maybe like hard to fill is something that that is going like off track so you can basically cl…

11. 28:52

    Chapter 11: We don't want their. 251s · Speaker 4

    We don't want their... the command center is it possible i know you said it doesn't it's not built for workflows in this moment is it possible to build it for workflows and it becomes like a central cause of work versus because what we do s…

12. 33:03

    Chapter 12: Yeah. So as we spoke as a problem, one of the biggest problem is we haven't captured skill data like throughout. 115s · Speaker 2

    Yeah. So as we spoke as a problem, one of the biggest problem is we haven't captured skill data like throughout. I mean, there is no place where a user or a associate has filled like what skills they have, how much rating they have. And you…

13. 35:00

    Chapter 13: Mercer and Lightcast are also two other partnerships that we have so we take everything we have identified few attributes out and from which becomes the single source of truth for all the skill data that you can see and then start filling by taking all the different sources in using the ontology that has been built so we start we create a position golden record and then yes what i thought in the interest of them is and quickly give you a three-step approach that is advanced it's a lot more but it's fine so like once you clean up your positions you clean up your positions That can be powered by internal data, can be powered by external data. 107s · Speaker 1

    Mercer and Lightcast are also two other partnerships that we have so we take everything we have identified few attributes out and from which becomes the single source of truth for all the skill data that you can see and then start filling b…

14. 36:47

    Chapter 14: And I think the position is the part that you were talking about initially as well, and then the employment is where they don't have business and whatnot. 89s · Speaker 2

    And I think the position is the part that you were talking about initially as well, and then the employment is where they don't have business and whatnot. So once you have the positions cleared up, then you can see who fits where, and if th…

15. 38:28

    Chapter 15: What I'm really interested though is digging into the skills and digging into the job descriptions and I'm hoping I'm just I want to dig into the job descriptions because We're going to leverage that position for so much other stuff that we would build. 78s · Speaker 5

    What I'm really interested though is digging into the skills and digging into the job descriptions and I'm hoping I'm just I want to dig into the job descriptions because We're going to leverage that position for so much other stuff that we…

16. 39:49

    Chapter 16: We can come and show you more specifics into that, but we'd love to also show you the montage into orchestration. 310s · Speaker 2

    We can come and show you more specifics into that, but we'd love to also show you the montage into orchestration. And then if we get to one problem stage... And then we will then build out, hey, here's our understanding of the problem. This…

17. 45:00

    Chapter 17: in this because again like big organization many different business groups acquisition merger happening day in day out of something we are doing it so do you do you want to deep dive on that piece of a work where like it's a very data engineering specific work that we are doing so do you want to deep dive there as well yeah we can quietly do this But I think that's important. 82s · Speaker 1

    in this because again like big organization many different business groups acquisition merger happening day in day out of something we are doing it so do you do you want to deep dive on that piece of a work where like it's a very data engin…