Retrieval, rebuilt from the evidence up.
Generic AI tools retrieve fragments and hope. Viore is built for knowledge where the wrong answer has a cost — and the right one has to be defensible.
active pilot corpora
published research
Your corpus has structure.
Generic AI tools treat it like a pile of text.
If you own a knowledge-dense corpus (hundreds of hours of expert material, thousands of pages of internal docs, a decade of institutional memory), you already know the problem. Your team can't retrieve it. Off-the-shelf tools hallucinate against it. Notebook-style products fragment it.
The chunk is the wrong unit.Experts don't reason in 500-token windows. They reason in claims, sources, and the consequences of being wrong.
Viore is built on evidence, not fragments. What that unlocks is a conversation best had on a call.
We measured the judge, not just the answer.
Before we built anything, we studied evaluation. Evaluative Fingerprintsis our open research on LLM-as-judge reliability: near-zero inter-judge agreement (Krippendorff's α = 0.042) alongside high within-judge stability (ICC up to 0.872). Judge identity is recoverable at 89.9%. That result changed how we think about trust, grounding, and what reliable retrieval should actually be optimizing for.
Built for corpora that matter.
Viore is in private pilot with a curated cohort. We're selective about who we take next, and honest about who we're not for.