Security-first AI for pharma

Stage: Live

Encrypted AI inference infrastructure running leading open-source models inside hardware-attested AMD SEV-SNP confidential VMs. Customer-managed keys and per-request attestation your client verifies — so even we are architecturally unable to decrypt your data during inference. Models on this infrastructure match leading proprietary models on independent public benchmarks. Multi-model debate — models discuss, disagree, converge — measurably outperforms single-model baselines on real production tasks. Pinned model versions for validated workflows. Swiss-headquartered. The foundation every product on this page runs on.

Stage: Live

GlassHood connects to a running system — today a solution on Google Cloud, read from Azure — and builds one live picture of it: every part, every connection between them and their health, refreshed every cycle. It merges what it discovers with the system's declared design, so the gap between the design and what actually runs is visible. Rules and anomaly detection raise the alerts; an AI overview then says in a few sentences how the whole system is doing and what needs attention, and each part has its own. Operators and business readers see the same dashboard, and nothing they do there changes the monitored system.

Stage: Live

AI test execution that reads existing test documents and navigates UIs autonomously. Recorded-script test automation breaks when the UI changes — and pharma UIs change often. Test teams either rewrite scripts continuously or fall back to manual testing. TestRobin adapts as the UI changes, produces audit-ready evidence reports, and doesn't require testers to code. Mixed UI + API testing; designed against ALCOA+, Annex 11, and 21 CFR Part 11 electronic-evidence requirements.

Stage: Piloting

BatchGuard checks manufacturing against its procedures. It reads the drug substance and drug product recipes and the batch records they produce, and checks every record and every field against your SOPs — not the 5% that manual review can sample. It does this during manufacturing, so a batch with a problem can be stopped early instead of being finished at full cost, and again after manufacturing, in the review before release.