Qwokka AI: Real-Time Employee Morale Analytics Platform
Co-founded and led the development of an AI-powered SaaS platform that measures and enhances employee morale in real-time. Secured three major clients, including Mexico's largest fintech company.
OVERVIEW
Qwokka AI is an innovative platform designed to quantify and improve employee engagement through real-time analytics. Currently in the angel stage, the platform is rapidly evolving based on early customer demand. As the Co-Founder and CTO, I spearheaded its development, securing major clients and ensuring seamless adoption.
Visit Project View on GitHub
Timeline
2024 - Present
Team Size
5 members
Tech Stack
PROBLEM SPACE
Employee disengagement costs billions, yet traditional surveys provide only delayed, self-reported snapshots of employee engagement. Without real-time engagement tracking, companies miss early burnout signals, leading to preventable turnover. By combining passive data (behavioral patterns, engagement trends) with active data (surveys, feedback), organizations can detect disengagement early and act before productivity and retention suffer. 🚀
Target Audience
HR departments and C-suite executives in medium to large enterprises
Market Size
$5.5 billion in the employee engagement market by 2025
67%
of employees are not engaged at work
$1.9T
Annual cost of disengagement in the US
37%
Higher sales for engaged teams
DISCOVERY
Discovery Process
Our team conducted extensive market research and interviewed over 50 HR teams, employers, and CEOs to deeply understand the challenges they face in monitoring and improving workplace morale.
Validation Methods
We used a combination of surveys, prototype testing, and beta user feedback to validate our product hypotheses and refine our solution. By iterating on early mockups and low-fidelity prototypes, we ensured our platform met user needs before full development.
Employees are concerned about surveillance, and companies want engagement analytics without violating trust.
eNPS (Employee Net Promoter Score) is Critical, But Companies Struggle to Improve It. Customers want more than just a score—they need AI-driven analysis to understand why engagement fluctuates and how to improve it.
Proactive vs. Reactive Management: Empowering leaders with continuous feedback loops enables proactive culture management—catching burnout risks and disengagement trends before they become crises.
PAINPOINTS
Painpoint 1
HR teams are always playing catch-up—they only realize engagement is dropping after it's a problem (e.g., turnover, burnout). They need predictive insights to act before disengagement escalates.
Painpoint 2
Ensuring compliance with privacy regulations like GDPR and CCPA
Painpoint 3
HR teams need engagement insights without overwhelming employees with feedback requests.
Painpoint 4
Companies guess which policies (e.g., no-meeting days, hybrid work models) improve engagement—but they lack a way to test effectiveness before rollout.
PRODUCT DESIGN
Feature 1
Engagement Experimentation Sandbox → Helping HR teams test & refine engagement strategies.
Feature 2
Real-time Engagement Dashboard → Providing HR teams with AI-driven insights on workforce sentiment and productivity blockers.
Feature 3
AI-Powered eNPS → Automatically tracking and explaining engagement score fluctuations.
Feature 4
Seamless Active & Passive Data Collection → Combining direct feedback with behavioral trends for a holistic engagement view.
Landing page.
Use cases.
Data analytics.
TECH STACK
frontend Technologies
RESULTS
clientsSecured
Secured 3 enterprise clients, including Mexico's largest fintech company.employeeEngagement
Increased employee sentiment reporting frequency by 2x compared to traditional quarterly surveys.AI Powered eNPS score
AI-powered eNPS tracking enabled customers to identify engagement trends rather than just relying on static scores.userAdoption
Achieved a 90% HR team and engineering team adoption within the first month of client onboarding.
LEARNINGS
Technical Insights
Developed a hybrid engagement tracking system combining passive behavioral data with active feedback loops for contextual insights.
Optimized eNPS analytics using machine learning to analyze score fluctuations and provide actionable recommendations.
Integrated predictive modeling to identify potential attrition risks before disengagement leads to turnover.
Built an Engagement Experimentation Sandbox using A/B testing methodologies to measure policy impact on engagement.
Project Management
Implemented agile methodologies to iterate rapidly and meet client needs
Coordinated cross-functional teams to deliver an intuitive and scalable product
Prioritized user feedback for continuous improvement of features and insights
