Core capabilities built for learning, not just logging
Every interaction improves recommendation quality. The product is designed as a system that observes, reasons, and adapts.
Multimodal meal understanding
AI interprets meal photos and natural language meal descriptions into structured nutrition data.
Adaptive nutrition estimates
Calories and nutrient estimates improve as the model learns your common foods, portions, and local meal patterns.
Smart clarification prompts
The assistant asks targeted follow-ups only when confidence is low, reducing input burden while preserving quality.
Body-response correlation engine
The system links meals, hydration, and symptom patterns to detect potential triggers and recurring effects.
Actionable weekly insights
Users receive concise summaries with trend explanation and practical next steps, not overwhelming dashboards.
Human-readable AI rationale
Insights include transparent reasoning so users understand why recommendations are made and can trust them.
Secure health record architecture
Sensitive nutrition, symptom, and body-metric payloads are stored using encryption-at-rest safeguards.
Operational edit controls
Teams and users can review, edit, and remove records to keep trend analysis accurate over time.
Data minimization
Only required fields are enforced; optional fields remain optional to reduce unnecessary collection.
Validation-first backend
Server schemas validate all event inputs before persistence, reducing corrupted health records.
Longitudinal intelligence
Trend quality improves as logs accumulate, so value compounds with daily usage.