How Vivo Works — Vivo by OmniScience

The Hard Part

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."

The Challenge

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.

01

Fragmented Data Sources

Trial data lives in 10–20+ systems. Unification requires deep clinical domain knowledge — not just ETL pipelines.

02

Variable Formats & Standards

CDISC, HL7, custom schemas, vendor-specific fields, legacy formats — harmonization requires clinical understanding, not just transformation.

03

Study-Specific Context

Protocol versions, amendments, visit schedules, endpoints, dose groups, and special populations vary per study. AI must reason in that context.

04

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.

05

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.

06

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.

07

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.

The Architecture

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

UI-First

Trial Home, Ask Vivo, dashboards, workflows — clinical teams use Vivo as their operating surface.

API

API / Headless

Ask Vivo and monitoring outputs power internal copilots, analytics workbenches, and reporting tools.

Agent

Agent-to-Agent

Vivo acts as a governed clinical domain agent within larger enterprise AI ecosystems and orchestration layers.

Vivo Intelligence Architecture

L1

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

L2

Unified Clinical Trial Data Layer

Harmonized · Governed · AI-ready · Role-aware · Full source traceability

↓ Protocol context · Amendment history · Visit schedule

L3

Agentic Reasoning Layer

Protocol-aware · Source-backed · Explainable · Role-aware · Blinding-enforced · Evaluated continuously

↓ Signals · Insights · Alerts · Evidence packages

L4

Monitoring & Evidence Layer

AI-RBQM · Risk signals · Issue tracking · Evidence packages · Audit records

↓ Human review · Governed action · API outputs

L5

Action Layer

Trial Home · Ask Vivo · Workflow tools · APIs · Portfolio views · Enterprise agents

Why Ask Vivo Is Only One Interface

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

Built for Regulated Clinical Decisions

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

AI Reliability is a Continuous Discipline

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.

The Team

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.

Clinical data science Agentic AI engineering Life sciences domain expertise

Meet the Team →

The governed intelligence layer for modern clinical trials.