Wilson4Q provides an on-device AI SDK that integrates with wellness applications to measure emotional fitness through its proprietary EFI Score™ engine. The technology analyzes sleep, stress, activity, and mood signals privately on the device, then recommends tailored sessions and quantifies pre- and post-intervention impact. This addresses the 96% churn rate plaguing wellness apps by shifting the industry's focus from engagement metrics to measurable outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
The wellness market is massive yet fundamentally broken: 95,000+ apps compete for attention while 96% of users churn within 90 days. Apps optimize for engagement rather than outcomes, leaving users unable to tell whether any intervention is actually working, and providers without meaningful data to improve their offerings.
Solution
Wilson4Q is an SDK that embeds into any wellness application — meditation, therapy, fitness, sleep, or nutrition — to deliver cross-ecosystem emotional fitness intelligence. It listens to everyday signals like sleep quality, stress markers, activity patterns, and mood indicators entirely on-device, with zero data leaving the phone. Its proprietary algorithm, the EFI Score™ Engine, converts these signals into a single measurable Emotional Fitness Index, then recommends the right session for the user and shows the before-and-after impact. This two-way intelligence flow helps users feel progress and helps apps learn what works, creating measurable outcomes that drive retention and engagement.
Target Audience
Primary customers are wellness application developers and providers across meditation, therapy, fitness, sleep, and nutrition sectors who need outcome measurement capabilities to differentiate their products and reduce user churn.
Features
- On-device EFI Score™ calculation with proprietary algorithm built on 30 years of research and theoretically validated with 2,000 participants, requiring zero data to leave the phone
- Cross-ecosystem signal input layer that reads sleep quality, stress markers, activity patterns, and emotional state indicators across multiple wellness domains
- Pre- and post-session scoring that quantifies intervention effectiveness for both users and app providers
- Privacy-by-design architecture with no accounts, no data sharing, and fully anonymized processing directly on the user's device
- Output layer including coaching recommendations, team readiness signals, and platform retention intelligence