Conventional systematic reviews are slow and costly, and they start going out
of date the moment they are published. Yet policymakers often need current,
context-specific evidence within narrow windows. Evidence TAP replaces one-off
reviews with living evidence databases that keep updating as new
research appears.
At its core is a traceable AI pipeline. It ingests the
literature across many sources and languages, screens it for relevance,
appraises study design, and extracts structured data. Every output remains
traceable to its original source. The pipeline runs on local, self-hosted
models, pairing keyword and semantic retrieval with a statistically principled
stopping rule.
97% recall against a large-scale manual review, in our
flagship study.
The pipeline adapts to the decision at hand. For urgent questions it can
synthesise all of the available evidence rapidly with minimal human checking,
flagging gaps for follow-up; where the stakes are higher, experts verify each
stage. Every verification is retained and feeds back into the models, so the
system keeps improving. The trade-off between speed and accuracy is
transparent and quantifiable.
It began in conservation, through Cambridge's Conservation Evidence
collaboration, and is now being applied to education. Health, climate and
other fields will follow. The longer-term aim is a global mesh of
self-hosted nodes that shares evidence equitably across and within countries.