Standard causal-inference tools ship a point estimate. Rosenbound ships the point estimate, the Rosenbaum-Γ sensitivity bound that quantifies how fragile it is, a five-method robustness pentagon, and a 21 CFR Part 11 ALCOA+ audit chain — all in one platform. Built for the pharmacovigilance and real-world-evidence teams who answer to the FDA.
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 →The FDA Sentinel Innovation Center's PRINCIPLED process (Desai, Wang et al., BMJ 2024;384:e076460) prescribes "a plan for robustness assessments including deterministic sensitivity analyses, quantitative bias analyses, and net bias evaluation."
Rosenbound's Rosenbaum-Γ bound is precisely this class of robustness instrument — reported as a default output on every study, not an afterthought. We're not selling a clever feature; we're selling the operational answer to a stated regulator expectation.
Aligned with the FDA RWE Framework (final guidance Aug 31, 2023) and the EHR/Medical Claims Data guidance (final Jul 25, 2024). See /regulatory for the full alignment map.
Same causal-inference engine + audit-trail ledger across four buyer types. Pharmacovigilance and real-world evidence are shipping now; drug-discovery and physician decision-support are on the roadmap. Each vertical has its own deep-dive page below.
For drug-safety directors at top-20 pharma and safety leads at major CROs. Replaces hand-coded FAERS-disproportionality workflows. Every adverse-event signal arrives with Rosenbaum bounds, MedDRA preferred terms, dual-attestation reviewer workflow, and 21 CFR Part 11 ALCOA+ provenance — regulator-ready by default.
Pharmacovigilance detailFor RWE methodology leads at academic medical centers and CROs. Five-method causal sensitivity pentagon as a single API call. Cohort DSL, OMOP CDM ingestion roadmap, target-trial-emulation protocol scaffolding, reproducibility certificate on every study. Replaces the hand-rolled methodologist work behind every observational pharma study.
RWE detailFor computational biology and target-discovery teams. The D-MPNN molecular-property prediction stack (validated on BACE / BBBP / HIV scaffold splits) is benchmark-locked. Active causal-MoA productization is on the 2027 roadmap; we maintain the benchmark capability as a published claim today.
Drug Discovery detailFor ICU and clinical-AMC physicians making individualized treatment decisions. Targeted FDA Class II SaMD via 510(k) with a Predetermined Change Control Plan; Q-Submission planned post first signed design partner. Available post-2027 after clinical-deployment validation.
Physician CDS detailBehind the login at the production app (and inside the live demo at rosenbound.ai) is a fully-working clinical-AI platform. Five modules, integrated, mutually-auditable. This isn't a demo — it's the platform pilot customers use on day one.
CSV upload with schema auto-detection, type inference, archetype suggestion, treatment/outcome/index-date mapping into an auto-generated Cohort DSL. The Cognitive Validation Report blocks ingestion of incoherent data and explains why — temporal, ontological, biological, and causal-acyclicity vetoes.
Submit a cohort, get five causal estimators on the same data: AIPW, doubly-robust ATT with overlap trimming, IV-LATE, neural counterfactual, and Rosenbaum bounds. Interactive Γ-slider on every run with the crossing-point explicit. Per-method drill-down with covariate balance + feature attribution.
Adverse-event signals with MedDRA preferred terms, SOC class, severity score, Γ-bound per signal, and a reviewer state machine (queued → triaged → in-review → quality-review → escalated/dismissed → signed-off). Filter by SOC, drug, term, severity, reviewer. Dual-attestation on high-severity sign-offs.
Every state transition cryptographically committed to a SHA-256 chained ledger. ALCOA+ aligned (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, Available). External-verifiable. Read-only export to pharma audit teams. Pharma uses our ledger inside their regulated workflow without bolt-on infrastructure.
Per-tenant dashboard for active studies, signal queue health, and reviewer throughput. Every completed study generates a reproducibility certificate: cohort definition hash, cohort data hash, certificate ID, git commit, pinned library versions — the exact re-derivation guarantee regulators ask for, in the UI.
The interactive demo at rosenbound.ai lets you upload a sample CSV, watch the Cognitive Validation Report fire, run a study, slide the Γ-curve, and download a reproducibility certificate — no login, no setup. Production-shape data + production-shape outputs.
Standard practice ships one ATT estimate. Rosenbound ships the full sensitivity pentagon plus quantitative bounds. Every number arrives with the uncertainty its method admits. Brief overview below; full methodology spec at /methodology.
Every metric below is from a production training run on the full data corpus — not smoke-run inflations, not cherry-picked seeds. Headline numbers shown; full tables (W1–W13, FAERS, ACIC22, DMPNN, HNSI) at /benchmarks with raw log references available to design partners under NDA.
Pharmacovigilance triage product satisfies all four criteria of 21st Century Cures Act § 3060: transparent algorithms, documented provenance, human-in-the-loop, independent review of the basis for the recommendation. Ships to pharma drug-safety customers in 2026 without 510(k).
Patent-pending audit module satisfies 21 CFR Part 11 § 11.10(a)–(k). SHA-256 chained ledger, monotonic timestamps, fsync + multi-writer protection, external-verifiable. Pharma clients use our outputs in their own regulated workflows without bolt-on audit infrastructure.
Structured around the Jan 2025 draft "Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making" — context-of-use definition, model-risk assessment, credibility-assessment plan, execution, documentation, and adequacy determination. Applied today on the PV and RWE products, not deferred to a future SaMD.
Q-Submission scoping for direct clinician-facing CDS deployment is planned post-first-signed design partner. Predetermined Change Control Plan (PCCP) drafted in parallel — the no-LLM-in-the-prediction-path frozen-feature architecture makes the PCCP modification space bounded and auditable.
Programmatic access to the audited platform — for CRO methodology leads, drug-safety directors, and RWE teams who need to script cohort uploads, run sensitivity-bounded studies, and pull reproducibility certificates without leaving their analysis environment. Pydantic v2 typed models with py.typed marker for full IDE autocomplete + mypy support. Apache 2.0 license.
The SDK is open-source. Platform access is not. Every protected endpoint enforces the following stack server-side — the SDK is a thin typed wrapper; the audit + isolation guarantees live in the platform:
get_current_user middleware. Tokens are JWTs minted from the in-product API-key management surface (admin role required). Tokens are never in the SDK source or in PyPI metadata.
require_permission(...) dependency on every protected route. Endpoints declare their required permission inline; missing permissions return HTTP 403 with no data echo.
TenantContextMiddleware + Postgres Row-Level Security policies. Cross-tenant reads and writes are server-rejected even with a valid token. The platform reads nothing outside the caller's organization.
AppendOnlyAuditLog entries + a mirrored audit_logs row + 21 CFR Part 11 ALCOA+ attestation fields. Ledger writes are fail-loud: a write that cannot anchor to the SHA chain rolls back the entire request.
pgcrypto; disaster-recovery runbook tested. BAA + DPA + MSA templates ready for partner signing.
cert = client.certificates.get(study_id)pdf = client.certificates.download_pdf(study_id)client.cohorts.create(csv=..., dsl=...) for each, and capture the validation status. Fail-loud on any rejection so your pipeline halts instead of silently dropping rows.
client.studies.create(...).run().get_results() wired into your existing Airflow / Prefect / cron stack. Pull pentagon estimates programmatically; route the Γ-bound + sensitivity intervals into your downstream dashboards.
Getting an API token. Founding Partners receive scoped API tokens during onboarding — issued from the in-product admin surface, tenant-locked, and limited to the RBAC roles each team member needs. Apply for the Founding Partner Program →
USPTO Provisional Filing
Founder Harsh Singh filed a USPTO provisional patent on March 22, 2026, sole inventor, covering the methodology stack that powers Rosenbound. Twelve-month conversion window to non-provisional or PCT runs through March 22, 2027. Clinical AI is the first commercial application of the underlying architecture.
Specific algorithmic and architectural detail is available to design partners under NDA after term-sheet signing. Capability-level description is shared openly; implementation specifics are reserved as the durable moat.
We're selecting five founding design partners for the Rosenbound platform across pharma drug-safety teams and CRO methodology groups. The economics are sized to fit a director's discretionary budget; the relationship is structured for a multi-year reference partnership.