Bio & high-stakes R&D AI
Research that can show its work.
AI now sits inside discovery workflows that touch the most valuable material a research organization holds. Glacis produces signed runtime evidence for each model-mediated step, and the prompts, sequences, and lab data behind it never leave your boundary.
What runtime evidence means here
Proof that protects the science it describes.
Proprietary-context protection
Zero-egress proof. Receipts demonstrate that prompts, sequences, structures, and lab data stayed inside your boundary, because only cryptographic fingerprints ever cross it. The evidence travels; the science stays home.
Audit trails for research decisions
Each model-mediated step produces a signed receipt: which model ran, under which policy, which controls executed. Receipts are hash-chained per project, so the history behind an AI-assisted decision is reconstructable and tamper-evident.
Review without disclosure
Receipts assemble into evidence packs that partners, IRBs, and internal reviewers can verify themselves. A reviewer confirms integrity and control execution without ever reading the underlying content.
Bring one research workflow.
The Sprint puts live runtime controls on a single named workflow and delivers signed receipts a partner or review board can verify themselves at /verify.