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Privacy-Proxy Module
A tested starting point for privacy-proxy module reduced repeated setup work.
We built a practical HRTech solution combining de-identified data stream, AI-powered sentiment prioritization, and manager intervention loops. The project delivered 24% Churn Reduction and 88% Prediction Accuracy.
Trusted by teams building ambitious products
Scale-up with 800+ engineers across 4 time zones facing aggressive headhunting and high replacement costs.
Language AI Specialist + Data Privacy Engineer + MLOps Lead embedded within People Operations and Engineering Leadership.
Moving from reactive exit interviews to early stay conversations triggered by behavioral data anomalies.
A practical solution designed around the client’s existing tools, teams, and day-to-day workflow.
The client suffered from silent churn, high-performing engineers resigning without prior warning or negative performance reviews. Existing engagement surveys were too slow (quarterly) and suffered from low participation rates among technical staff.
The biggest issues were knowledge leakage, replacement tax, and privacy anxiety. The team needed a faster, clearer way to manage the work while keeping the right checks in place.
Quarterly, manual self-reporting with high bias and 40% participation rates.
Live, passive data collection of work-rhythm signals across the dev-stack.
Manual reviews of Slack or Email that compromised trust and individual privacy.
Models process hashed data patterns without ever accessing message content.
Diagnostic data captured after the engineer has already decided to leave.
Engagement alerts triggered 30-45 days before at-risk behaviors culminate in resignation.
Mathematical noise injection into data ensures that individual engineers cannot be identified, even if the database is breached.
AI-powered workflows generate personalized coaching tips for managers based on the specific burnout signals identified in their team.
Live identification of PR-response time spikes or silent GitHub activity drops as a proxy for technical disengagement.
Tested foundations helped the team spend less time on setup and more time on the parts that made this product useful.
A tested starting point for privacy-proxy module reduced repeated setup work.
Reusable work for workplace sentiment module let the team focus more time on the client’s specific needs.
This made it easier to add talent monitoring platform without rebuilding common foundations.
A tested starting point for retention finops module reduced repeated setup work.
A straightforward before-and-after view of what changed for the team and their customers.
Early interventions saved valuable engineering staff who would have previously resigned without warning.
Identifying at-risk engineers with high accuracy allowed for targeted management resources.
Lower churn directly translated into millions of R&D budget preserved for product innovation.
Coretus solved the impossible: they gave us visibility into engineering morale without breaking developer trust. We’ve reduced churn by 24% by having the right conversations at the right time, powered by data we didn't know we could use safely.