Vedron is building the operating layer for practical AI. We turn elite human judgment into structured signals that teach models how to perform better at scale.
Our proprietary evaluation environment— ZORA —replaces synthetic tests with deliverable-level tasks, creating the gold standard for model competence. Every interaction compounds into alignment, safety, and agentic workflows that enterprises can trust. ZORA is where AI stops guessing and starts working—unlocking trillion-dollar productivity gains.
New research
Myopic points: how frontier models fail expert STEM problems
What GPT-5.5, GPT-5.4, GPT-5.3 and Claude Opus 4.8 got wrong on expert STEM problems, why, and what it means for verifiers and post-training.
Read the reportZORA– v2 leaderboard
GPT 5.1
Anthropic Claude Opus 4.5
Gemini 3 Pro (utilising accelerated model as appropriate)
Grok 4
Llama 4 (Scout/Maverick/Behemoth as appropriate)
Positioning
Most benchmarks measure generalized capability; ZORA measures decision-adjacent competence. We run domain-specific evaluations that reflect the artifacts professionals rely on including valuation write-ups, legal research notes, analytical proofs, design specifications and scored within methodologically rigorous pipelines that emphasize provenance and auditability.
What we measure
Grounding & Evidence Use
source adherence, citations, and factual integrity
Reasoning & Structure
coherence, completeness, and logical traceability
Operational Utility
accept-amend-deploy fitness within real workflows
Risk & Controls
proper surfacing of material risks and mitigation stance
Initial domain coverage
Finance
- valuation memos
- sensitivity analyses
- market theses
- diligence briefs
Law
- research notes
- client advisories
- matter outlines
- policy synopses
Science & Math
- proof sketches
- experiment plans
- error/sensitivity analyses
Engineering
- architecture diagrams
- protocol specs
- reliability notes
Method highlights
Task design
deliverables defined with practitioner co-authors and mapped to real time/quality constraints
Source packs
curated evidence to enforce grounded outputs and minimize hallucination risk
Rubric frameworks
multi-criterion scoring aligned to domain norms and inter-rater reliability procedures
Governance
controlled environments, reviewer calibration, and disagreement auditing to surface edge cases
Trust posture
Privacy-preserving operations: Human-supervised scoring– Secure evaluation environments