Mirobody

Mirror Your Body in Data

One standard, every source, one complete picture of you — accumulating toward a model that can see where your health is going. This is Mirobody.

Open infrastructure Proven in production
5,000+ Users 500+ Daily active Powering Theta Health

The standard reference for human health data

Every number your body produces, in one language.

Mirobody is the canonical reference for human health — roughly two thousand standardized indicators, clinical and lifestyle, that any source can be resolved into. It is the layer that turns scattered readings into one coherent picture of you.

~2,000 canonical indicators 600,000+ raw sources distilled LOINC · SNOMED · RxNorm foundation
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Canonical indicators
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Raw indicators distilled
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Standard terminologies unified · LOINC · SNOMED · RxNorm
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Clinical + lifestyle coverage

The problem

Your health speaks in a hundred dialects.

Every lab, device, and app names things its own way, in its own units, against its own reference ranges. The same measurement arrives as five different records — so no system, and no person, ever holds a single source of truth about one body.

One measurement · five spellings

Glucose, fasting mg/dL Fasting glucose mmol/L FBG lab PDF bloodGlucose wearable GLU-F EHR

The moat

Anyone can build a health app. The hard part is the map underneath.

We didn't crowdsource a vocabulary — we distilled one. Starting from the major North American terminologies, our clinical team hand-annotated and reconciled the raw corpus into a set that actually describes a human body: every common lab, exam, and vital, plus the lifestyle signals wearables generate.

Source corpus 600,000+ Raw standard indicators pulled from LOINC, SNOMED, RxNorm and more — the North American terminology base.
Human curation By physicians Doctors label, merge, and reconcile duplicates — resolving names, units, and reference ranges by hand.
The standard ~2,000 Canonical indicators covering medical and lifestyle — the most complete standard map of a human body we know of.

Wider than any consumer health platform

Distinct health data types exposed
Mirobody ~2,000
Apple Health ~190
Google Health Connect ~40

Approximate counts, linear scale. Apple Health ≈ 190 HealthKit sample types (120 quantitative + 70 category, iOS 26); Google Health Connect ≈ 40 record types across 7 categories. The point is the order of magnitude, not the decimal.

AI Map

Once the standard exists, the reconciling is automatic.

Point any data at Mirobody — a lab PDF, an EHR export, a wearable feed, a number typed into chat. AI Map resolves each reading to its canonical indicator, name, and unit. When it's unsure, you decide. And it remembers what you decide.

Map

Any source, standardized

Names in any language, values in any unit, from any device or document — each is matched to the one canonical indicator it belongs to, and converted to canonical units on the way in.

Edit

You have the final say

When a match is ambiguous or wrong, correct it in a single tap. Your body's record is yours to curate — the AI proposes, you confirm.

Remember

Mapped once, mapped forever

Every correction is recorded, so the same indicator never has to be resolved twice — and each confirmation sharpens the shared map for the next import, from you and everyone after.

One glucose reading, three ways

6.1 mmol/L 110 mg/dL 1.10 g/L
converts to 110 mg/dL canonical

Unit normalization

Same reading. One unit.

Standardizing the name isn't enough — the units have to agree too. Every value is converted into one canonical unit, so mmol/L and mg/dL stop being two different numbers. Only then do trends, comparisons, and thresholds actually line up.

A slice of the canonical table

theta · standard_indicators — excerpt
std_idCanonical nameUnitSystemKind
ldl_cholesterolLDL Cholesterolmg/dLlipidlab
hemoglobin_a1cHemoglobin A1c%glycemiclab
resting_heart_rateResting Heart Ratebpmcardiovascularlifestyle
respiratory_rateRespiratory Ratebreaths/minrespiratorylifestyle
sleep_efficiencySleep Efficiency%lifestylelifestyle
phenotypic_agePhenotypic Ageyearsderivedderived

Human deep data

Standardize enough of your data, for long enough, and something new appears: a complete, continuous account of one human body. We call it your Mirobody.

The health equivalent of a company's financial statements — not a snapshot from a single visit, but a longitudinal ledger deep enough to reason about, and in time, to forecast from. This is the asset that compounds the longer you hold it.

Depth of record · over time

One body, accumulating toward a model of itself.

First importYears of record

Advanced indicators · Peraxis Lab × Theta

Beyond measurements, toward biological meaning.

Peraxis Lab and Theta are building a new layer of health intelligence at the intersection of AI, biomedical science, and longitudinal human data.

We transform standardized health records into higher-order indicators designed to reveal patterns that individual measurements cannot capture alone — including Phenotypic Age, Fracture Risk, Sleep Score, and Stress Score.

Biological aging● live
−4.2 yr vs. calendar

Phenotypic Age

A blood-derived estimate of how old your body is functioning — your biology's age, not your birthday's.

Musculoskeletal health● live
18 % / 10 yr

Fracture Risk

Five- and ten-year fracture probability, derived from bone density and clinical inputs for older adults.

Sleep & recovery● live
82 / 100

Sleep Score

Duration, continuity, and overnight recovery signals resolved into one measure of how well your body restores.

Physiological stress● live
34 / 100

Stress Score

Autonomic load read from heart-rate variability and related signals — the strain your body is carrying.

Peraxis research pipeline◇ expanding

More signals are coming.

New indicators across aging, metabolic health, cardiovascular function, recovery, and resilience. The research is ongoing — new indicators will continue to emerge as the science advances.

Readouts shown are illustrative. Advanced indicators are decision-support tools — not a diagnosis or a substitute for clinical care.