AI fitness coach: from missed fatigue and form breaks to +68% retention.

Pulse is an adaptive AI-powered fitness platform that personalizes workouts using biometric data, behavioral insights, and coaching logic. Unlike typical fitness trackers that report what happened, Pulse interprets body signals and adjusts guidance in real time — acting as an intelligent coach rather than a scoreboard. Built for a U.S. fitness technology company (under NDA) that had extensive user data but lacked a product team capable of turning those signals into a system users would actually stick with.

platform

Mobile (iOS & Android) - Flutter

verticals

B2C, Healthtech, Fitness Tech, AI

team & duration

~6 FTEs, ~11 months

services

Digital product strategy, Digital product management, Digital product development

Challenge.

The fitness app market was crowded but impersonal. Generic, static training plans didn't adapt to individual bodies — ignoring fatigue, recovery, sleep, stress, and hormonal cycles. This caused low retention, motivation collapse, hidden injury risk, and weak user LTV.

Key research findings that shaped the product:

72% of participants described motivation as cyclical — enthusiasm followed by fatigue and disengagement.

49% of beginners noticed early discomfort before injuries but had no way to interpret those signals.

56% of users said rigid training plans rarely reflected reality — performance changed based on recovery, nutrition, and energy.

43% of coaches lacked visibility into athletes' condition outside the gym.

The core industry flaw: more data ≠ better results. Users needed intelligence, not information. The client had millions of sessions and biometric signals but lacked a product team to turn them into a system users stay with.

Solution.

Built Pulse — an adaptive AI fitness platform with four core pillars:

Adaptive AI engine that reshapes workouts daily based on progress, fatigue, and recovery, powered by OpenAI.

Holistic health integration connecting sleep, hydration, heart rate, and stress signals via Apple HealthKit and Google Health Connect (Apple Watch, Garmin)

Hybrid coaching system pairing machine intelligence with human coaches through real-time chat.

Injury prevention system detecting biomechanical deviations before they become injuries.

Development was evidence-driven: thousands of simulated workouts across running, strength, recovery, and group dynamics trained the algorithms before real-user testing. The team treated development like athletic training — each iteration made the system sharper and more responsive.

Impact.

Retention

+68%

Training plateaus

−74%

Minor injuries

−63%

LTV: Explicitly improved — stronger engagement and higher user lifetime value driven by retention gains and adaptive personalization. RevenueCat integration enabled subscription optimization.

Retention: +68% — the headline metric. The key insight was that fitness progress is cyclical, not linear. Guiding users through dips (rather than ignoring them) was the primary retention driver.

Conversion: Not directly quantified, but increased workout frequency and the “felt like it knew me” user reaction indicate strong activation-to-habit conversion. Amplitude analytics enabled conversion funnel optimization.

CAC: Not directly measured in scope. However, +68% retention mechanically improves CAC payback period. OneLink attribution tracking supports acquisition channel optimization.

UX: Users consistently reported the app “felt like it knew them.” The hybrid coaching model plus distraction-free dark UI created emotional connection beyond typical tracker experiences. The interface was intentionally designed to fade into the background so attention stays on the workout, not the app.