How it works: the interpretation methodology

The algorithm is deterministic: the same input always yields the same result. No language model takes part in the conclusions — AI is used at exactly one narrow step, reading text off an uploaded report, and its output is then processed by ordinary code.

Below is the whole path from a number on a form to the finished interpretation, step by step.

Step 1. The marker catalogue

The catalogue is the foundation: the B12 cluster, iron and its stores, blood count, vitamin D, omega-3, minerals, B vitamins and vitamin A, thyroid, lipids, glucose metabolism, kidney, liver, inflammation, protein and conditionally essential compounds.

Each entry records what the marker measures, why it matters on a plant-based diet, which units it appears in, what different labs call it, and which sources the range rests on. All of it is published openly in the marker guide.

Step 2. Units

Values are converted to the marker's canonical unit. Numerically equivalent units are mapped without arithmetic — ng/mL and µg/L, pg/mL and ng/L, µIU/mL and mIU/L. Cyrillic spellings are recognised alongside Latin ones.

One thing the tool deliberately does NOT do: it never guesses mass-to-molar conversions that would require a molar mass. Such a value is flagged for checking rather than converted on a hunch. A silently wrong number is more dangerous than no number.

If a unit cannot be read, the typical unit of the recognised lab is substituted — and that value is likewise flagged for checking.

Step 3. Scoring a marker

The converted value is placed on a scale: deficient, below optimal, optimal, above optimal, markedly high. For some markers the bounds depend on sex — iron stores, for instance, are read on different scales for women and men.

The context you provide — sex, age, diet type and years on it, pregnancy and breastfeeding, menstruation and its heaviness, intense training, B12 supplementation, height and weight — feeds into the reading and the resulting suggestions.

Step 4. Cross-marker rules — the real difference

A single marker almost never tells the truth alone. The rules look at the whole picture and fire only when every value they need is actually present — so a partial panel yields fewer conclusions, never false ones. Characteristic examples:

  • High folate masks B12 deficiency — the classic plant-based trap: anaemia does not develop while neurological damage progresses.
  • A "normal" serum B12 alongside raised MMA or homocysteine points to functional B12 deficiency that serum B12 alone cannot show.
  • High CRP makes ferritin falsely normal: inflammation inflates it and hides genuine iron deficiency.
  • Low zinc mimics iron-deficiency symptoms, while excess zinc displaces copper and causes anaemia in its own right.
  • A low omega-3 index together with missing cofactors — iron, zinc, magnesium — indicates limited ALA-to-EPA/DHA conversion.
  • Low magnesium blunts vitamin D, and high PTH with low vitamin D suggests secondary hyperparathyroidism.

Step 5. Summary and personal plan

The output is an interpretation: what sits in the optimal band and what does not, which interconnections were found, and which basic tests are missing. A plan is built alongside it — dietary sources, what the literature says about supplements and how they interact (iron and tea, zinc and copper, vitamin D with fat), and when a retest makes sense.

The plan is a structured digest of the sources for your particular panel, not a prescription. Responsibility for any action remains yours; see the Terms of Use.

The role of AI

AI is responsible for exactly one step: reading an uploaded PDF or photo and transcribing marker names, numbers and units verbatim, plus recognising the lab. Scoring, rules and the plan are not entrusted to it — ordinary deterministic code does that work.

The split is deliberate: a language model can misread, and you will catch that when checking against your form, but it cannot quietly distort the interpretation logic. Manual entry always works and involves no AI at all.

Updates and corrections

Ranges and rules change as their sources do. Every guide page shows its last update date, and corrections backed by a publication are reviewed and folded into the catalogue.

FAQ

Does the result depend on a neural network?

No. The model only transcribes numbers from an uploaded form. Scoring, cross-marker rules and the plan are deterministic code: identical input always gives identical output. With manual entry, no AI is involved at all.

What if I enter only some of the tests?

The interpretation gets shorter but not false: a rule fires only when every value it needs is present. The tool additionally lists which basic tests are missing to complete the picture.

Why does the tool ask me to check units?

Because some conversions cannot be done correctly without a molar mass, and some units cannot be read off the form. In those cases the tool does not guess: it flags the value so you can check it against the original.

Updated: 22 July 2026

See also