Our core innovation isn't autonomous AI — it's the opposite. An intelligence layer does the relentless work of watching the metabolic signal; a specialist makes every decision. Speed of software, judgement of a clinician.
Most health AI tries to replace the clinician. We built the inverse: AI that amplifies one, with a hard rule that no clinical action ships without human sign-off.
It's the difference between a chatbot that guesses and a system a practising endocrinologist will actually stake their name on. That trust boundary — human-approved, evidence-grounded — is the defensible core of AiHealth.
CGM, body composition, labs and wearables stream into one continuous patient timeline — 288 glucose points a day, not one a quarter.
An engine computes variability, dawn phenomenon, meal response and time-in-range before any record reaches a model — so the AI reasons over signal, not raw noise.
MMIQ folds the picture into one calibrated 0–100 metabolic index, with a priority score that triages who needs attention first.
The care agent proposes the plan in clinical language, grounded in the patient's own data.
Dr. Bhograj reviews, edits and approves. No clinical action ships without the specialist's sign-off.
MMIQ isn't tuned on intuition. It's built on a research program analysing real continuous-glucose data at scale — and the findings have already changed how we score risk.
Qualitative summary of our metabolic research program; full effect sizes available to partners under NDA. Manuscripts in preparation.
Where standard CGM reports stop at an average, MMIQ goes further — and the research above is exactly why.
We separate how much of a patient's risk comes from basal, post-meal and overnight glucose — three problems, three different fixes.
A dedicated nocturnal score catches the ~1 in 4 "in-range" patients who are unstable while they sleep.
A 70–140 mg/dL "tight range" alongside the standard 70–180 — closer to how healthy metabolism actually behaves.
The diabetes index — a defensible 0–100 score of glucose-driven metabolic health, calibrated for clinical triage.
The obesity index — body-fat, skeletal muscle, visceral fat and waist in one score, so we track fat lost and muscle kept.
Continuous glucose distribution and variability — the metric that actually tracks outcomes.
The moat isn't the model. It's the data, the trust boundary, and the economics of reaching everyone.
Years of real continuous-glucose and meal data on Indian metabolism — a corpus no off-the-shelf model has, and that compounds with every patient.
Doctor-in-the-loop is exactly what clinicians and regulators require. Pure-AI competitors can't safely cross it — we designed the product around it.
The engine needs little more than a glucometer and a phone — so it extends from city clinics to rural camps without re-engineering. Reach is the strategy.
Built and used by a practising endocrinologist across a growing clinic network. Detailed evidence and methodology available to partners under NDA.