QIW turns high-stakes questions into source-bound answers and defensible rankings. Every answer arrives with its sources and a trust score — so "how do we know?" stays answerable.
Watch the 20-minute storySCOPE defines an AI problem precisely enough that the definition travels through an organisation without fragmenting. QIW turns its measurable E leg from a promise into a number.
The same high-stakes questions get asked again and again — and answered from scattered chats, PDFs and one person’s memory.
Decisions rest on unverifiable claims; nobody can say how well-supported an answer is, and the reasoning does not survive re-telling.
Every answer arrives with its sources, a trust score, and a defensible ranking — reproducible on demand.
Source-bound (no ungrounded model memory), snapshot-dated, your own API keys, your data isolated by row-level security.
A trust score per answer, abstention and position-bias diagnostics, 94% posterior intervals on rankings — success is numeric, not vibes.
Curated questions answered in batch by source-bound LLMs with web search — with findings, a best source and a trust score. Export as PDF, Word, Markdown, BibTeX, RIS.
Candidates with citable dossiers are compared pairwise by LLM judges; a Bayesian model turns that into a posterior with a 94% interval — overlapping intervals honestly read as "not decided".
See the interactive demo →Dossiers are filled automatically from LLM + web search — with seven gates (citation integrity, counter-evidence, snapshot …) instead of hand-seeding. A human sample-reviews.
How many single verdicts turn into a defensible order is hard to explain and easy to show. Two interactive demos do exactly that.
Two dossiers, one verdict. You can see why an uncertain candidate jumps a long way and a well-evidenced one barely moves — and which comparison tells you most.
Order K dossiers in a single call. One call buys K(K−1)/2 pairwise constraints — the K slider makes the trade immediately visible.
Demos on invented data — the same mathematics QIW runs over real, sourced dossiers. See the demos
“entreater” — one who entreats: to ask earnestly, to make a pressing request.
From Old French “entraitier”, from Latin “tractare” — to handle, to treat (from “trahere”, to draw).
QIW is built for the entreater — the person who brings the questions that matter and wants them handled with evidence, not guessed at. The mark is an E drawn by connecting the dots: E for Evidence — and the graph is exactly what QIW does: connecting questions to sources, candidates into a ranking.