For drug-safety directors at top-20 pharma and safety leads at major CROs. Adverse-event signal triage with Rosenbaum-Γ bounds on every estimate, MedDRA preferred terms, a dual-attestation reviewer state machine, and a 21 CFR Part 11 ALCOA+ audit ledger that pharma uses inside their own regulated workflow.
Disproportionality scores (PRR, ROR, IC) are point estimates. Reviewers spend hours deciding whether a signal is real, an artifact of co-reporting, or a known indication-bias. By the time the PSUR submission goes out, the methodology footnote is a paragraph of qualitative caveats.
"Identifying and addressing the presence of confounding and other forms of bias is critical… planned sensitivity analyses to assess the robustness of study findings." — FDA Non-Interventional Studies draft guidance, March 21, 2024.
Rosenbound delivers the sensitivity analysis as a default output. Every signal arrives with the Rosenbaum-Γ bound that quantifies how strong an unmeasured confounder would have to be to flip it. Reviewers move from "this looks suspicious" to "Γ = 1.06, very sensitive — escalate" in seconds.
Each feature is in production today and visible to design partners at first login. Try them on a synthetic cohort at rosenbound.ai.
Every adverse-event signal in the reviewer queue carries its Rosenbaum-Γ sensitivity bound. Sort by fragility. Triage by signal AND by how robust the evidence is.
Version-pinned MedDRA browser with verbatim-to-PT auto-suggest. Signals carry the Preferred Term, System Organ Class, and the LLT match where applicable. WHODrug roadmap.
State flow: queued → triaged → in-review → quality-review → escalated/dismissed → signed-off. High-severity sign-offs require two independent reviewers. Role-based filters for "My queue", "Team queue", "All signals".
Methodology and provenance block on every signal-detail page distinguishes corpus-level model AUROC from per-signal estimates. No silent overclaiming — reviewers see exactly which population a metric refers to.
Every state transition committed to a SHA-256 chained ledger. Attributable, Legible, Contemporaneous, Original, Accurate — built in, not bolted on. Read-only export to pharma audit teams.
The system refuses to ingest incoherent data and explains why — temporal vetoes (treatment-before-diagnosis), ontological vetoes (dose-above-FDA-max), causal-acyclicity vetoes, biological vetoes. Bad data gets blocked at intake, not silently confidence-graded.
FDA MedWatch 3500A export today; full ICH E2B(R3) ICSR XML on the 90-day roadmap. Bidirectional import in the same release window. CIOMS I as international add-on.
AuthKit/WorkOS with SAML and OIDC. Email + Google + Microsoft + GitHub + Apple SSO out of the box. Three default roles (admin, reviewer, viewer); custom roles via the founder Slack.
Upload a sample CSV, watch the Cognitive Validation Report block bad signals, slide the Γ-curve on a flagged signal, sign it off, see it materialize in the audit ledger. No login. rosenbound.ai →
The PV triage product satisfies all four exemption criteria: for a healthcare professional, advisory not directing, transparent algorithm with documented provenance, independent-review-of-basis enabled via the Γ-curve and methodology block. No 510(k) required for 2026 ship.
Audit module satisfies § 11.10(a)–(k) without operator configuration. SHA-256 chained ledger, monotonic timestamps, fsync + multi-writer protection, external-verifiable. Pharma uses our outputs inside Argus/Vault Safety/LifeSphere workflows without bolt-on audit infrastructure.
Sensitivity-bounded reporting is exactly what the FDA RWE Framework (final Aug 2023) + the EHR/Claims Data guidance (final Jul 2024) + the Non-Interventional Studies draft (Mar 2024) are now asking for. Rosenbound provides it as the default output.
Rosenbound does not replace Argus, Vault Safety, or LifeSphere. We sit beside them as the methodology + audit-defensibility layer. Signal-evaluation sections feed your PSUR/PBRER/DSUR/PADER directly; we don't author the whole report.
Watch the product walkthrough at rosenbound.ai — three moments that define the platform: the Cognitive Validation Report refusing incoherent data, the live Γ-bound sensitivity visualization, and the reproducibility certificate generated on every study. The full platform stays gated for Founding Partners.
Watch the preview →
pip install rosenbound
— Official Python SDK for programmatic access: cohort upload, sensitivity-bounded study runs, and reproducibility certificate retrieval. Apache 2.0; Pydantic v2 typed; py.typed for IDE autocomplete + mypy. Platform access gated by Bearer token + RBAC + tenant scoping — the SDK is open, the audit substrate is not.
Founding Partners pay $100K year 1 (vs $250K list), $175K years 2–3, then the locked Founding rate of $250K/yr that never passes future price increases. Plus direct founder Slack, quarterly roadmap input, named-reference recognition, waived integration setup, and first access to new modules.