The study exists in multiple incompatible versions.
Protocol, operations, analytics, MIDD, safety, data management, and vendor systems each maintain a partial view of the same study and often reconcile only at formal milestones.
Clinical development intelligence
Run clinical evidence as one governed operation.
StromaOS connects trial analytics, clinical operations, model-informed drug development, real-world evidence, and safety in one execution environment. Protocol, study, site, patient, endpoint, operational, exposure-response, and safety decisions retain the evidence and authority that produced them.
Shared study context
Connected clinical evidence
Governed AI execution
Traceable human decisions
The clinical development operating problem
Connected questions are answered in disconnected systems.
Clinical development depends on the interaction between scientific evidence, protocol design, quantitative modeling, study operations, patient data, site performance, safety, and external evidence. Those functions frequently operate with separate tools, definitions, timelines, and decision records.
Signals emerge before the operating model can act on them.
Site, enrollment, quality, safety, endpoint, and exposure-response signals may be visible in one function while the context required for action remains elsewhere.
Evidence is repeatedly packaged instead of inherited.
Teams spend time rebuilding context for governance meetings, protocol amendments, dose decisions, interim reviews, regulatory interactions, and cross-functional actions.
Automation increases faster than execution control.
Analytical copilots and agents can accelerate review, monitoring, and drafting, but without explicit data boundaries, method validation, escalation rules, human authority, and traceability they create new operating risk.
How the solution works
Clinical work moves through one governed decision chain.
StromaOS coordinates study context, analytical methods, operational signals, domain agents, quality gates, and accountable human review from planning through execution and evidence handoff.
STEP 01
Define the development question
Protocol, endpoint, population, dose, site, enrollment, risk, amendment, analysis, or evidence gap.
STEP 02
Assemble the shared context
Protocol, study data, operations, sites, safety, models, prior studies, RWE, regulatory commitments, and decisions.
STEP 03
Run governed evidence work
Analytics, statistical methods, MIDD, operational intelligence, safety review, RWE, and approved AI agents.
STEP 04
Apply evidence and quality gates
Data sufficiency, method validity, reproducibility, confidence, deviation, exception, and review requirements.
STEP 05
Exercise accountable authority
Clinical, statistical, medical, operational, safety, pharmacology, and governance decisions.
STEP 06
Preserve and reuse
Inputs, analyses, versions, assumptions, approvals, actions, outcomes, and cross-study learning.
Program and study control
See the program as one operating system.
Clinical leaders need a coordinated view of scientific, analytical, operational, safety, and regulatory readiness without replacing the specialist systems used by each function.
StromaOS connects those systems through common entities, definitions, workflow states, decisions, risks, evidence gaps, milestones, and authority.
Feasibility starts from a governed cohort definition, with counts, attrition and source coverage at every criterion before money is committed to a design.
Model a clinical program workflow
The analysis, the assumptions and the authority that accepted them stay joined. A result you cannot reconstruct is not a result you can file.
Governance throughout clinical development
Automation can accelerate the work without bypassing clinical authority.
StromaOS separates retrieval, analysis, recommendation, action, approval, amendment, release, and submission. Clinical, statistical, safety, pharmacology, operational, and regulatory authorities retain accountable decisions while AI operates inside explicit policy and quality boundaries.
Explore the Governance Spine01
Purpose-bound data access
Study, patient, site, country, vendor, and external data are accessed only for approved users, purposes, workflows, jurisdictions, and operating contexts.
02
Validated analysis paths
Methods, estimands, code, statistical assumptions, diagnostics, model versions, quality criteria, and reviewer acceptance remain part of the execution record.
03
Separated AI authority
An agent may detect a signal or prepare an amendment package without receiving authority to alter a protocol, change a dose, close a site, or submit a response.
04
Human review and escalation
Thresholds, confidence, risk severity, exceptions, unresolved uncertainty, and policy determine which clinical and governance authorities must decide.
05
Evidence Data Trace
Inputs, transformations, code, models, prompts, methods, versions, decisions, approvals, actions, and downstream consequences remain reconstructable.
Start with one study, one program, or one constrained clinical workflow.
Connect the study sources and specialist systems already in use. Define the decision, authority, methods, quality gates, and required human review. Then run the work as a governed clinical operation.