Your smartwatch quietly watches your health trends and alerts your AI doctor the moment something looks off, before you even feel sick.

Prana integrates with consumer wearable devices (smartwatches, CGMs, blood pressure monitors) to ingest continuous biometric data streams including heart rate, HRV, SpO2, blood glucose, sleep patterns, and activity levels. Time-series anomaly detection models—likely leveraging LSTMs or transformer-based architectures—establish personalized physiological baselines for each patient and flag statistically significant deviations in real time. When the system detects a concerning trend (e.g., a sustained upward drift in resting heart rate combined with declining HRV), it triggers a clinical alert pathway: first notifying the patient through the app with contextualized guidance, then escalating to a human physician if the anomaly persists or worsens. This closed-loop system transforms passive wearable data into actionable clinical intelligence, shifting care from reactive symptom response to proactive intervention.

It's like having a smoke detector that doesn't wait for the fire—it smells the wiring getting warm and calls the electrician for you.

An AI doctor interviews you about your symptoms before the real doctor steps in, so by the time you talk to a human they already know your whole story.

Prana deploys a conversational AI agent—likely built on large language models fine-tuned on medical dialogue corpora—to conduct structured and semi-structured patient interviews before a human physician is engaged. The system collects chief complaints, history of present illness, past medical history, medications, allergies, and review of systems through natural language conversation via chat or voice. NLP pipelines extract structured clinical entities (symptoms, durations, severity scores, anatomical locations) and map them to standardized medical ontologies (SNOMED-CT, ICD-10). A triage classification model then assigns acuity levels and generates a pre-visit clinical summary for the physician, complete with differential diagnosis suggestions and recommended next steps. This automation handles the bulk of routine intake—sore throats, medication refills, follow-ups—while intelligently escalating complex or ambiguous presentations to human clinicians with full context already assembled.

It's like having the world's most thorough nurse practitioner do your entire intake interview at 3 AM without ever needing a coffee break.

Your AI doctor looks at your entire health history and daily habits to predict what might go wrong next year and builds a plan to stop it before it starts.

Prana's most ambitious ML application combines longitudinal electronic health record data, continuous wearable biometrics, lab results, medication adherence signals, and conversational AI interaction logs into a unified patient health graph. Predictive models—likely gradient-boosted ensembles or transformer-based architectures trained on large clinical datasets—estimate individualized risk scores for conditions such as Type 2 diabetes progression, cardiovascular events, hypertensive crises, and mental health deterioration over 3-, 6-, and 12-month horizons. These risk scores feed into a care plan generation engine that produces personalized, evidence-based intervention recommendations: lifestyle modifications, medication adjustments, screening schedules, and specialist referral triggers. The system continuously updates predictions as new data flows in from wearables and patient interactions, creating a living care plan that adapts in real time. Explainability layers (SHAP values or attention-based explanations) surface the key risk drivers to both patients and physicians, enabling shared decision-making grounded in transparent AI reasoning.

It's like having a financial advisor for your body—except instead of predicting market crashes, it predicts health crashes and rebalances your wellness portfolio before you lose anything.