retrieving
Video ingestion activePrivate pilot · cohort open
For healthcare & life sciences any field where a wrong answer has a cost

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.

19,281 momentsevidence indexed from
active pilot corpora
arXiv preprintEvaluative Fingerprints
published research
The surfaces
Product surfaces

Four ways to work with trusted evidence.

Find the answer, map the connections, follow the pathway, share the proof.

01Ask

Ask your knowledge agent.

Get trusted answers with cited evidence, confidence scores, and key moments.

View surface
What is the most effective dose of GLP-1 for glycemic control?
Agent answerHigh confidence

Start GLP-1 therapy low and titrate. Most patients achieve glycemic control with 0.25–1.0 mg weekly. Higher doses improve effect sizes up to ~1.2% A1C.

Sources 12Anchors 18Confidence 92%
0:35Titration & dose optimization
1:42Efficacy across populations
2:15Safety & GI tolerability
02Explore

Explore connected knowledge.

Map concepts and uncover relationships across studies, guidelines, and outcomes.

View surface
Knowledge graph of concepts connected to GLP-1 therapies
+ Find path from GLP-1 tosearch a concept…
03Pathways

Follow reviewed pathways.

Step-by-step clinical and operational pathways with evidence, audio, and summaries.

View surface
  1. 01AssessPatients & context
  2. 02Start lowInitiate & set expectations
  3. 03TitrateFind the lowest effective dose
  4. 04MonitorSafety & response
Audio summary2:15
1212985+

Start low, stay low: GLP-1 Titration Pathway

An evidence-based approach to initiating and titrating GLP-1 therapy with safety checkpoints and monitoring.

View full pathway →
04Publish

Publish with evidence.

Create reviewable knowledge assets, move through review, and share with control.

View surface
DraftReviewApproved
Article draft

Optimizing GLP-1 Dosing for Efficacy, Safety, and Sustainability

Last edited 2 hours ago
  • Start low, stay low2 sourcesHigh support
  • Titration & monitoring3 sourcesHigh support
  • Safety considerations2 sourcesMedium support
  • Long-term outcomes4 sourcesHigh support
The gap

The chunk is the wrong unit.

Experts don't reason in 500-token windows. They reason in claims, sources, and consequences.

Judging chunks ignores how experts think—so agreement collapses.

Inter-judge agreement
α = 0.042
Almost none
Within-judge stability
ICC 0.872
Almost perfect
Judge identity is recoverable at 89.9%.
The research

Before we built anything, we studied evaluation.

Evaluative Fingerprints is our open research on LLM-as-judge reliability.

Near-zero inter-judge agreement (α = 0.042) sits alongside high within-judge stability (ICC up to 0.872). That result changed how we think about trust, grounding, and retrieval.

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.

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