When it comes to care, it pays to be proactive.

Until now, tackling attrition had been something of a puzzle. Establishing the cause of an employee's departure felt like a herculean labor, with uncertain answers that always arrived too late; incredibly disparate factors, from diversity to burnout to peer relationships, all contributed to a worker's sense of fulfillment, inclusion, and decision to stay or leave.

Far from sustainable, this approach has wreaked havoc on many an enterprise's bottom line.

Without a centralized method of monitoring talent risk factors or gauging the efficacy of remedies, most enterprises resigned to simply spend their resources filling vacated positions and training new hires — while simultaneously struggling to meet prior levels of client demand. Far from sustainable, this approach has wreaked havoc on many an enterprise's bottom line. It's no wonder, then, that customers who lower their rates of attrition with Praisidio calculate an ROI upwards of 5000%, with the platform typically recouping its cost in less than a month.

With powerful AI, there's no more feeling like you're going it alone.

Human resources professionals have long been searching for a way to ground their employee care decisions in clear, comprehensive data. Praisidio was made for them.

Caring for individual employees at scale is now possible, no matter the size of your organization. Depend on features including detailed individual timelines, insights spanning five critical risk dimensions, and the option to attach guiding notes and explanations to employee profiles.

• Rely on powerful machine learning algorithms that correlate trends across workload, compensation, connections, growth, and recognition to likely outcomes.
• Mitigate risk factors associated with gender, location, ethnicity, and manager anomalies, which could result from systemic bias or other addressable issues that fall under DEI compliance initiatives.
• Generate up-to-date attrition risk reports that reflect the efficacy of implemented recommendations.