Forecast QA Gate for Niche Research Publishers

A pre-publication quality gate for niche research publishers, independent analysts, and paid newsletter operators who publish claims about markets, companies, policy, or technology with outcomes that resolve in days to weeks. Authors submit a draft note plus cited sources; the system extracts concrete claims, pressure-tests them using AE's six-pattern autopsy taxonomy, and forces explicit resolution criteria, time windows, and transmission assumptions before publication. After release, claims are automatically graded against public evidence on the declared schedule, creating a living accuracy ledger for each publication and analyst. This is not generic editing or RAG: the product's value is turning fuzzy analyst prose into auditable, reality-graded claims and showing where failures came from via the taxonomy. Buyers are those who monetize trust directly through subscriptions or premium research and can use a public or client-shared track record as a commercial asset. The loop is fast enough when focused on short-horizon claims, and the output compounds into differentiated credibility rather than one-off copy polish.

A forecast QA gate for niche research publishers is a credible wedge because it converts analyst trust into a measurable asset, fits a growing publishing market, and offers a differentiated capability generic AI tools do not: pre-publication falsifiability plus post-publication reality grading.

Engine verdict: GATHER_MORE_SIGNAL (WORTH_SKIMMING). Differentiated trust infrastructure, but no real buyer proof and draft-sharing/accountability aversion make this premature.