An overview of what goes into developing our AI.
It comes from not only using the best algorithms out there, but actually pushing the envelope pushing the frontiers of advancement over here. A simple example is focusing on equal opportunity algorithms that enable systems to learn across every protected class and ensuring that the behavior of the system is same for man versus woman. Young versus old, no matter who you are, right. It comes from the analytics of people. It comes from the transparency that we bring to everyone. It comes from the patterns that we have filed over the years.
So when we started the company, I had no idea what HR is. I didn’t even know what is ATS. And I would struggle to figure out what is HR is versus HRMS. But the interesting thing because of that was we never thought of the actual fragmentation. We never thought we are solving a talent acquisition problem or a talent management problem or a diversity problem or succession planning problem or lnd problem or career development problem or a payroll or performance problem, right? We always thought it is a talent.
Enterprise need the best talent that can do the world wherever that talent is. So you can’t think of talent as a silo and the reason why that is important is once you start cutting through this entire lifecycle that is when you have the best understanding of the data. So that now you can think of who I am attracting who I’m hiring, who I am promoting, when growing in my company, one of the skill inquire I’m retaining over time right and what is because tell me that story what is consumed me that is story. So that has been our big focus area and a different approach to solving this problem.