Founded in 2019, Praisidio is a venture capital-backed Talent Risk Management AI software company. Headquartered in the San Francisco Bay Area, the company’s patent-pending platform is the first of its kind talent risk management platform which provides early employee attrition risk detection, root-cause insights, recommendations, and retention action workflows to materially improve retention and employee work life.

In an interview with Manage HR Magazine, Ken Klein, Co-founder, and CEO, and Vahed Qazvinian, Co-founder and CTO, talks about protecting enterprise companies with data-driven, augmented intelligence tools that help manage talent at large.

In your interactions with leading companies, what are the challenges they face now in the talent management space, and how is Praisidio effectively addressing those issues?

Ken Klein: Talent Management has a significant impact on an organization’s bottom line and can cost a large organization hundreds of millions of dollars per year. However, what is frequently overlooked is that the impact is not solely financial. It also has a significant effect on a variety of other aspects of the business, including growth, profitability, culture, brand, and even market capitalization.

Praisidio's platform helps companies discover talent risk and insights early so they can be prioritized and addressed. It assists in identifying employees suffering from bias, burnout, disconnection, or stagnation and enables retention actions by delivering personalized care plans for employees. For example, many of our customers have adopted robust diversity and inclusion initiatives to care for employees with diverse backgrounds, agendas, and ethnicities.

Vahed Qazvinian: In times of COVID, having a thousand employees working from home is equivalent to having thousand remote offices. Due to this, newly hired employees have not met their manager in person or had the opportunity to build a relationship within the last two years. As a result, working remotely has made it more challenging to offer care to employees. Using Praisidio, managers gain a better understanding of the difficulties their employees face enabling them to develop strong relationships with their employees.

Moreover, apart from supporting employees with work-life issues such as workload, equity, connection, and career progress, organizations typically realize a $50 savings in attrition for every dollar invested in our product, thus ensuring a phenomenal ROI.

Please shed some light on the talent management solution that you offer in terms of its methodology, features, and benefits involved.

Vahed Qazvinian: Detecting an issue in a huge organization with thousands of employees is tough. We help our customers identify early insights on employee happiness and attrition by combining metadata from many systems, normalizing it, stacking it on top of one another, and running complex machine learning algorithms.

Additionally, Praisidio was built from the ground up with enterprise security and data privacy in mind. The application undergoes regular penetration testing and security reviews by white hat security firms. Our customers entrust us with sensitive personal data that we protect with encryption in transit and at rest. In transit, data is encrypted using bank-grade TLS 1.2, the safest method available today. At rest, data is encrypted using 256-bit encryption via native Amazon Web Services capabilities. Our AWS S3 buckets are encrypted using server-side AWS-managed keys. Praisidio is GDPR and SOC 2 Type II compliant.

What are Praisidio’s top retention practices?

Ken Klein: There are three pillars to employee retention best practices. The first is to become data-driven as it is one of the biggest factors in improving staff performances. The second is the transition from a reactive approach to employee resignations to one that is considerably more strategic and proactive. Thirdly, programmatic techniques or initiatives are unsuccessful or ineffective when it comes to retention and need to be personalized at scale. Our Talent Retention Management tool, Praisidio embodies these best practices being: data driven, proactive, and personalized.

Could you elaborate on a success story to provide an understanding of how your offerings play out in the real world?

Vahed Qazvinian: We assisted a technical firm in examining bias by supporting them in recognizing prejudices with specifics risk factors. For example, individuals from underrepresented groups were much more likely to attend meetings, had a greater workload but were significantly underpaid. As this organization had thousands of employees, it was really difficult to pinpoint the location of the issue, but we were able to do so utilizing machine learning and artificial intelligence.

What sets you apart from the market competition?

Ken Klein: There are three factors that set us apart from the competition. The first is that we leverage numerous data sources and also aggregate and stacking this data. Second, we are leveraging AI in a unique way, focused on uncovering insights that are both explainable and actionable, which is extremely differentiated from other approaches in the sector. Finally, we provide personalized best-in-class employee retention practices, recommendations, and workflows. As a result, employers can boost employee happiness, wellbeing, and career longevity.

We Assist Our Customers To Identify Early Insights On Employee Happiness And Attrition By Combining Metadata From Many Systems, Normalizing It, Stacking It On Top Of One Another, And Running Complex Machine Learning Algorithms

‍

What does the future hold for the company?

Ken Klein: We are constantly innovating our products. We are working on expanding employee retention workflows. The second objective is to increase our channels, which involves investments in sales, marketing, and partnerships, in order to continue fueling our rapid client growth. We have completed a round of funding led by Signalfire and SemperVirens in June 2021. As a result, we are now well-capitalized to expand our staff in support of our growth objectives.

‍