A transparent view into the intelligence we're adding to Complete Coach — so you can plan your business around what's coming.
Founder Program members get access to everything in the Core Platform module. The founder build process will focus on shaping the AI Check-In Analysis layer and the business management features that help coaches operate at scale.
Founder Program coaches will help shape how Complete Coach reviews client check-ins, surfaces risk signals, highlights progress trends, and turns weekly data into faster coaching decisions.
We'll use founder feedback to refine the systems that help coaches manage leads, onboarding, pipelines, tasks, client operations, and the business side of scaling a coaching company.
AI EXPANSION
Machine learning models trained on coaching data to surface risk signals before clients go quiet or disengage.
Identify clients showing disengagement patterns — missed check-ins, declining response quality, reduced training compliance — weeks before they cancel.
Score each client's likelihood of completing their current program based on early adherence signals, so you can intervene with the right clients at the right time.
Surface clients who are hitting consistent milestones and showing high satisfaction signals — the optimal moment to introduce additional services.
AI EXPANSION
Continuous analysis of training data across your entire client base to surface patterns invisible to the human eye.
Aggregate and compare volume, intensity, and frequency patterns across clients and time periods to identify what programming approaches drive the best results.
Cross-reference sleep, stress, and performance data to flag clients trending toward overtraining or under-recovery before performance degrades.
Map the days, times, and conditions under which individual clients are most and least compliant — then use this to personalise programming timing.
AI EXPANSION
Deep nutritional intelligence that goes beyond calorie counting to understand what is actually driving body composition outcomes.
Detect early-stage compliance drift in macro tracking before it compounds into week-long deviation — with suggested intervention messages ready to send.
Identify the combination of training load, caloric intake, and metabolic adaptation signals that typically precede a plateau, with a 2–3 week lead time.
AI-generated nutrition adjustment suggestions based on recent biometric trends, training volume changes, and historical response data for each client.
Track vitamin, mineral, and micronutrient intake over time, then highlight likely gaps, recurring patterns, and opportunities to improve client nutrition quality.
Start with the platform today and be first in line when AI Expansion launches.