A federated deep learning engine trained on 2M+ patient spectra — delivering personalized predictions while keeping every user's data private and sovereign.
A three-tier architecture: edge inference on the device, personalization in the cloud, and federated model improvement across the global population.
Traditional AI health platforms require uploading raw patient data to central servers. iGlutek's federated architecture inverts this model: the AI model travels to the data, not the other way around.
Each device trains locally on its own biometric history, then shares only encrypted gradient updates — mathematical summaries of what the model learned, never the underlying health data itself. The global model improves with every user without any user's privacy being compromised.
Generic health ranges kill precision. iGlutek builds a unique physiological model for every user, calibrating across 30+ biometric dimensions.
The first two weeks establish your personal reference ranges for all 11 biomarkers — accounting for your unique metabolism, circadian rhythms, and lifestyle patterns.
Rolling 30-day LSTM recalibration adapts to seasonal changes, weight fluctuations, medication effects, and long-term metabolic shifts without user intervention.
Anomaly detection triggers alerts up to 48 hours before values cross clinical thresholds — giving you time to act before symptoms appear.
Cross-biomarker pattern recognition identifies compound metabolic events that single-metric monitoring would miss entirely — e.g. pre-diabetic glucose-insulin-cortisol signatures.
Pharmacokinetic compensation adjusts baselines for known drug effects on NIR absorption — reducing false positives for users on chronic medication regimens.
Clinician portal provides structured trend reports, event logs with Bayesian confidence scores, and HL7 FHIR-compatible export for EHR integration.
Built on HIPAA-eligible cloud infrastructure with multi-region redundancy, automated failover, and real-time anomaly alerting at every layer of the stack.
WebSocket streams from device → Kafka broker → schema validation → time-series partitioned storage.
Kubernetes autoscaling inference cluster — GPU-backed training, CPU-optimized inference. P99 latency <500ms.
Rule engine + ML anomaly detector → push notification → physician portal → optional EMS integration (Phase 3+).
Scheduled gradient aggregation across device fleet → differential privacy noise → global model update → OTA push.
Full Business Associate Agreement support. PHI handling compliant with US federal health privacy law.
EU data residency, right to erasure, data portability, and DPA agreements for all European operations.
Information security management system certification — covering all cloud infrastructure and development processes.
Annual third-party audit of security, availability, and confidentiality controls across our cloud platform.