How we work
Our numbers are only as good as how we gather and check them. This is exactly how a figure becomes a published data point — and what we refuse to do.
The data verification flow
- 1
Collect
arXiv · FDA · clinical registries · official announcements (varies by field)
- 2
Normalize
into one standard shape: value · date · actor · source
- 3
Candidate
the end of automation — the figure plus the exact source sentence
- 4
Human verification
Never automateda person checks the primary source, then approves or rejects
- 5
Record as a time series
append-only — never overwritten, corrections kept in history
- 6
Publish with its source
linked to the primary source, with date and actor
What sources we use
Structured, citable sources first: arXiv, OpenAlex, official company announcements and roadmaps, public registries. API-provided data wherever possible.
Which metrics, and why
Only objective, industry-agreed indicators — qubit counts, gate fidelity, clinical phases, benchmark scores. We never invent a composite “% there” score.
How a number gets published
Automation only ever produces candidates. A person checks the primary source and approves before anything is published — and we record who verified what, when.
What we don’t do
No invented scores. No unverified figures. No cross-field comparisons (quantum vs drugs is meaningless). No copied article text — only the figure and its source link.
Fact vs interpretation
Tracker numbers are neutral and achromatic. Analysis is labelled and coloured — clearly “our read”, never disguised as fact.