The governed intelligence layer for modern clinical trials.
Vivo is not a dashboard with an AI button. It is a five-layer clinical trial intelligence architecture, purpose-built for clinical development.
Clinical trial AI is only as good as the data foundation beneath it.
Building AI for clinical development is not a prompt engineering problem. It is a data architecture, domain expertise, and governance engineering problem. The AI interface is the last 10%. The first 90% is building the foundation that makes it trustworthy.
"The hard part is not answering one question. The hard part is answering the right question from the right data, for the right user, with the right evidence, at the right time."
Seven hard problems clinical AI must solve.
Generic AI fails in clinical trials because it is not built for any of these. Vivo is purpose-built for all seven.
Fragmented Data Sources
Trial data lives in 10–20+ systems. Unification requires deep clinical domain knowledge — not just ETL pipelines.
Variable Formats & Standards
CDISC, HL7, custom schemas, vendor-specific fields, legacy formats — harmonization requires clinical understanding, not just transformation.
Study-Specific Context
Protocol versions, amendments, visit schedules, endpoints, dose groups, and special populations vary per study. AI must reason in that context.
Role & Blinding-Aware Access
A study sponsor, medical monitor, data manager, and site coordinator each see a different part of the trial. Blinding integrity must be preserved.
Source Traceability
Every insight, answer, and alert must trace back to specific source records. You cannot inspect, audit, or act on AI outputs you cannot verify.
Continuous Change
Trial data changes daily. Subjects enroll, visit, report AEs, get queries, and produce lab values in real time. The intelligence layer must keep pace.
Clinical Decision Support
The output of clinical AI is not just text. It supports safety decisions, submission evidence, regulatory filings, and patient care. The standard is higher.
Five layers. One governed intelligence platform.
Vivo's architecture follows a strict one-way flow: source data is ingested, harmonized, reasoned over, monitored, and surfaced as governed action — with source traceability preserved at every step.
Vivo operates in read-only mode. Source data is never modified.
Deployment patterns:
UI-First
Trial Home, Ask Vivo, dashboards, workflows — clinical teams use Vivo as their operating surface.
API / Headless
Ask Vivo and monitoring outputs power internal copilots, analytics workbenches, and reporting tools.
Agent-to-Agent
Vivo acts as a governed clinical domain agent within enterprise AI ecosystems and orchestration layers.
Data Sources
EDC · CTMS · TMF · Safety DB · Labs · Imaging · eCOA · IRT · Wearables · Biomarkers · Omics · Vendor Files · Sponsor Warehouses · Documents
Read-only ingestion · Source records preserved
Unified Clinical Trial Data Layer
Harmonized · Governed · AI-ready · Role-aware · Full source traceability
Protocol context · Amendment history · Visit schedule
Agentic Reasoning Layer
Protocol-aware · Source-backed · Explainable · Role-aware · Blinding-enforced · Evaluated continuously
Signals · Insights · Alerts · Evidence packages
Monitoring & Evidence Layer
AI-RBQM · Risk signals · Issue tracking · Evidence packages · Audit records
Human review · Governed action · API outputs
Action Layer
Trial Home · Ask Vivo · Workflow tools · APIs · Portfolio views · Enterprise agents
The difference between a chatbot and a clinical operating layer.
A standalone clinical chatbot
Answers questions from whatever data it was connected to
No continuous monitoring or alerting
No source traceability or evidence packaging
No role-based or blinding-aware access
No protocol or amendment context built in
No workflow, issue, or action layer
Vivo as a clinical operating layer
Source-backed answers from unified, governed trial data
Continuous AI monitoring — the trial alerts you, not just you querying
Every answer links to source records with provenance
Role-aware RBAC with blinding integrity controls
Protocol, amendment, visit schedule, and endpoint context built in
Alert → issue → assignment → evidence → review → audit trail
Clinical AI must be explainable, permission-aware, and reviewable.
Vivo is designed for the regulatory and quality standards that govern clinical trial data and AI use. This is not a compliance layer added after the fact — it is built into the architecture.
Role-based access controls (RBAC) — every user sees data appropriate for their function
Blinding-aware access — treatment arm and endpoint data protected in active trials
Source traceability — all AI outputs link to specific source records and transformation history
Human review — AI assists, humans decide and sign. Governed automation, not autonomous AI.
AI evaluation — answer quality, source grounding, stability, and user feedback monitored continuously
Audit trails — every action, query, alert, issue, and resolution timestamped and attributed
Prompt monitoring — usage patterns reviewed for study integrity
Answer correctness monitoring
Automated and human-in-the-loop evaluation of output quality and source grounding
Response stability evaluation
Detect if AI outputs shift unexpectedly across model or data updates
User feedback integration
Clinical user feedback on answer usefulness captured and fed into evaluation loops
"AI reliability is not a one-time test."
It is a continuous product discipline built into Vivo's operating model.
Built by a team that understands clinical data and AI.
OmniScience was founded by clinical data scientists, AI engineers, and life sciences domain experts. Before Vivo, the team spent years building clinical data systems, running data management programs, and working directly inside the trial operations challenges Vivo now solves.
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