THE PROBLEM 

Losing a top performer costs a company anywhere from 50% to 200% of that person's annual salary—in recruiting costs, lost productivity, institutional knowledge, and team disruption. It is one of the most expensive events in corporate life, and in the overwhelming majority of cases, it was entirely predictable. The signals were there—in engagement patterns, performance trajectories, compensation drift, internal mobility signals, and manager relationship quality. Companies simply had no system to read them in time. 

THE OPPORTUNITY 

We're looking for startups building AI-powered retention intelligence platforms that give HR and executive teams early warning signals—predictive attrition scoring at the individual level, built on behavioral and operational signals that are already flowing through enterprise systems. The critical insight is that the data already exists: in calendar patterns, communication graphs, system access logs, performance metrics, and compensation histories. The startup that builds the model to synthesize it into actionable, timely intelligence will save enterprises millions per year, per cohort. For the savvy investor, this is applied ML with immediate, measurable ROI—the easiest sale in HR tech. 

Analysis & Implications 

The average cost of replacing a mid-level software engineer is $50,000–100,000 when you account for recruiting fees, interviewing time, onboarding cost, and productivity ramp of the new hire. For a senior engineer or technical lead, it is higher. For a key account manager at a SaaS company, losing them often means losing the accounts that depended on the relationship. Yet most organizations find out an employee is leaving when they receive the resignation letter. By then, the cost is already incurred. 

The data that would have predicted the departure was available months earlier. Research on attrition consistently identifies reliable leading indicators: declining participation in internal communications, reduced attendance at discretionary meetings, stagnation in compensation relative to market rates, manager relationship deterioration, and—the most reliable signal of all—increased engagement with external professional networks. Most of these signals flow through systems the enterprise already operates. Nobody is synthesizing them in real time. 

What the winning product looks like: a system that ingests signals from across the enterprise stack—calendar data (are they attending fewer optional meetings?), communication patterns (has Slack or email participation dropped?), performance data (has their review trajectory flattened?), HRIS data (are they due for a promotion that hasn't arrived?)—and synthesizes these into individual attrition risk scores with enough lead time for meaningful intervention. Not an anonymous engagement survey. Not a manager sentiment check-in. A predictive model that identifies specific individuals likely to leave in the next ninety days, and what intervention has the highest probability of changing the outcome. 

The privacy architecture is not optional—it determines whether the product is deployable. Employees will not accept covert surveillance, and if they discover it exists, the engagement damage is worse than the attrition it was trying to prevent. The winning product is transparent about what it monitors, narrow in what it uses, and built around aggregate behavioral signals rather than individual surveillance. The framing is "we're measuring whether we're treating people well," not "we're detecting flight risk." 

The ROI calculation writes itself. A company with 1,000 employees and 15% annual attrition is replacing 150 people per year. If the platform costs $100,000 annually and prevents ten avoidable departures, it has returned five to ten times its cost. The first CHRO you show this math to will sign before the meeting ends. 

Early players—Visier, Workday Peopllytics, Culture Amp—have built analytics tools that describe historical attrition. They are useful for understanding what happened. The predictive layer, built on real-time signals rather than historical surveys, is the next generation. It doesn't exist in a genuinely useful form yet. Build it.