Integrations Updated 2026-08-01 View as Markdown

Bloodwork

Upload a lab report PDF and its markers become a structured record: values, units, reference ranges, and a trend once you have more than one panel.

Bloodwork is where behaviour meets biology. A habit column tells you what you did; a marker tells you what it did to you, on a timescale of weeks rather than days.

Uploading

Upload the PDF from the labs view. Text is extracted from the document in your browser, then that text — not the file — is sent for parsing into structured markers.

That split is deliberate. Lab reports carry your name, date of birth and provider details in the header, and the file itself never needs to leave your machine for the markers to be read out of it.

Extraction handles the common panel formats: metabolic, lipid, hormone, thyroid, inflammatory, vitamins and minerals. You review what was extracted before it is saved, which matters because lab PDFs vary and an occasional marker lands in the wrong row.

What you get

A panel overview — every marker with its value, unit, reference range, and whether it sits inside or outside that range.

Trends — once there are two or more panels, each marker gets a series and a sparkline. This is the point at which bloodwork becomes genuinely useful; a single panel is a snapshot, and a snapshot cannot tell you which direction you are moving.

Statistics — change since the last panel, change since the first, and rate of change per marker.

Correlations against behaviour. Daily behaviours are tested against markers over a biologically plausible exposure window, so a marker that responds over eight weeks is correlated against eight weeks of behaviour rather than yesterday's meals. This is what connects "I have been eating better" to "and here is what moved."

Asking about it

get_lab_data returns the full panel with precomputed insights, or a single marker with its history. Fuzzy matching handles the usual shorthand — "vit d" finds Vitamin D, "testosterone" finds both total and free.

Markers also enter the cross-module correlation graph, so a question about a biomarker can be answered against your habits, nutrition, WHOOP data and genetics together rather than in isolation.

Genetics interaction

If you have uploaded a methylation profile, the platform cross-links the two: your genetic variants predict which markers are worth watching, and those predictions get tested against your actual panels. A variant that suggests you should watch homocysteine becomes a hypothesis, and your bloodwork either supports it or does not.

Cadence

Two to four panels a year is typical, and enough to build a trend. More often than every eight weeks is usually not informative — most markers move slower than that, and you end up reading noise as signal.

Limits

This is not medical advice. The platform reports your values against reference ranges and against your own history, and identifies patterns. It does not diagnose, and it is not a substitute for the clinician who ordered the panel.

Extraction can be wrong. Review what was parsed. The PDFs are inconsistent and the extraction is good rather than infallible.