Bio & high-stakes R&D AI
Research that can show its work.
AI now sits inside discovery workflows that touch valuable research material. Glacis can produce signed operational records for model-mediated steps routed through a configured boundary. Whether prompts, sequences, and lab data remain inside a particular environment depends on that deployment’s routing, configuration, and egress controls.
What runtime evidence means here
Evidence designed around the science it describes.
Proprietary-context protection
Configure the evidence record to use hashes, labels, and selected metadata instead of raw research payloads. A receipt alone does not establish payload locality: reviewers must also examine the deployment’s routing, storage, telemetry, and outbound connections.
Audit trails for research decisions
Covered model-mediated steps can produce signed receipts stating the model, policy, control claim, and reported outcome. Hash links can expose later changes within the presented chain; they do not prove source truth, complete workflow coverage, or control effectiveness.
Review without disclosure
Receipts can assemble into evidence packs for partners, IRBs, and internal reviewers. A reviewer can check supported signatures and covered fields without routine access to raw payloads, while separately assessing scope, signer provenance, effectiveness, and any disclosure needed for the review.
Bring one research workflow.
The Sprint scopes configured controls and signed records to a named workflow, with explicit routes, exclusions, payload handling, and fields a partner or review board can check at /verify.