Philosophy
Global Data Aggregates
Last updated: 28 April 2026
Every person who uses Teample is, in some small way, a data point about tea. How much they use, how often they brew, what they reach for in winter versus summer. Over time, taken together, that adds up to something Teample has not seen before: a real, lived, quantitative picture of how people actually drink tea. Building something useful from that is one of Teample's longer-term ambitions. This is how we intend to do it.
Individual data stays individual.
Nothing you record in Teample — your brews, your packs, your shelf, your spending — is ever shared with other users, surfaced in comparisons, or used to profile you individually. That does not change as Teample grows. The boundary between your data and the collective layer is permanent, not provisional.
Collective patterns, not personal records.
The global statistics Teample aims to build are aggregates: distributions, averages, tendencies drawn from many people's behaviour, with no path back to any individual. The question we want to be able to answer is not "what does this user do?" but "what do people generally do with this kind of tea?" Those are entirely different questions, and the architecture is designed to keep them that way.
Useful to everyone, derived from everyone.
The goal of the aggregate layer is to make Teample more useful to every person who uses it. When you add a tea to your shelf, a sensible starting estimate should already be there. When you look at a catalog entry, relevant context — how much leaf people typically use, how long sessions tend to last — should be available. That context can only come from the people who have actually used those teas, and sharing it back is what makes the platform more than a personal notebook.
Transparent about what is collected and why.
As the aggregate layer evolves, Teample will be explicit about what data feeds into it, how it is anonymised before aggregation, and what the resulting statistics are used for. This statement will be updated when there is something concrete to say. We do not intend to build the data layer first and explain it later.
This is a direction, not a finished system. What exists today are the foundations: the data structures, the privacy principles, the commitment to keeping individual and collective data cleanly separated. What comes next will be built on top of those, not instead of them.