AI uplifts your best researchers. Until it doesn't.
Your researchers are already using AI
Every lab, every team. The question isn't adoption — it's verification. An analysis reported by Retraction Watch in May 2026 found fabricated references in about 1 in 277 PubMed-indexed papers from early 2026. Who catches that before it reaches your publications?
Silent failures compound
A doctoral student submitted a notebook with 300+ cells. 180 were AI processing its own error messages. No flags. No warnings. Just confidently wrong output.
Single-model blind spots
If your researchers use one AI model, they inherit that model's failure modes. Verification requires architectural diversity.
Source: Retraction Watch, 7 May 2026, reporting a Lancet letter on fabricated references in PubMed-indexed papers.
Institutional-grade verification for AI-generated research.
Cross-model adversarial review
Independent model families attack outputs from different architectural perspectives, producing a claim-support audit, contradiction memo, and reviewer-ready risk register. No single model's blind spots.
Citation verification
DOI verification against live databases catches fabricated and mismatched references before reviewers do.
Adversarial claim testing
Claims that survive multi-model adversarial attack are stronger publications. Claims that don't survive save your institution a correction.
Verification at institutional scale.
Axion verifies AI outputs in constrained scientific domains where hallucination has real consequences. Medical research, regulatory submissions, patent claims, grant applications — fields where "close enough" isn't. Your researchers get AI's productivity gains. Your institution gets verification that holds up.
Institutional scope for R&D verification.
Submit a sample of AI-generated research output from your institution. We run it through cross-model adversarial review and show you what survives.