Your coordinators scramble before every monitoring visit. We end that in 24 hours.
The AI Visit Prep Report reviews your de-identified trial documents the way a monitor will — deviations, essential-document gaps, and risk flags surfaced before the visit, not in the findings letter. Human-reviewed. Delivered within 24 hours. First report $2,500 · additional reports from $1,500.
80%+ of sites report 21+ hours of source-document prep for high-complexity studies — Tufts CSDD · Applied Clinical Trials, 2026
Clinical trials are run on software from another era.
Every sponsor pays $500K to $1M per trial for platforms that still require CRAs to compare paper source documents by hand, surface adverse events after the fact, and stitch together data from eight disconnected vendors. And at the sites running those trials, coordinators still lose days before every monitoring visit — pulling binders, chasing signatures, and hoping nothing surfaces in the findings letter.
wrldOS unifies clinical trial execution.
wrldOS is built to end fragmented clinical trial execution. Today, that ships as a command layer above the validated systems you already run — the AI Visit Prep Report below is live now. The destination: CTMS, EDC, eTMF, IRT, monitoring, SDV, safety, queries, deviations, documents, audit trail, and AI agents in one operating system.
Predictive, not reactive.
In the product preview, AI risk scoring demonstrates 48–72 hour early warnings on serious adverse events. Enrollment is forecast weeks ahead, not reported weeks behind. Protocol deviations surface themselves.
Incumbents report after the fact. wrldOS predicts before it matters.Unified, not fragmented.
Command Center consolidates signals from every vendor into one pane. The data you need to run a trial stops living in eight dashboards and thirty-seven daily emails.
Legacy platforms store and manage trial records. wrldOS moves trial execution forward.AI-native, not bolted on.
wrldOS agents operate inside the trial OS — reading signals, identifying risk, drafting next actions, routing work for human review, and preserving the evidence trail. Five specialized AI agents plus deterministic scoring engines, 120+ API endpoints, full 21 CFR Part 11 audit trail.
The incumbents' AI is a press release. Ours is the architecture.The platform is real. Explore the interactive preview.
Not renderings. Not slides. An interactive product preview built on mock clinical trial data — 120+ endpoints, 30+ database tables, five specialized AI agents plus deterministic scoring engines. Walk through it like a sponsor's Chief Medical Officer would on a Monday morning.
Built for research sites and site networks. Upload de-identified trial documents and wrldOS surfaces what the monitor would flag — deviations, essential-doc gaps, site risk — human-reviewed and delivered within 24 hours, so your team walks into the visit ready instead of scrambling. First report $2,500 · additional reports from $1,500 · checkout live.
Three forces converged. The window is open.
AI-native clinical trial infrastructure wasn't possible five years ago — and won't be uncontested five years from now. The moment is specific, and it is right now.
Regulators have moved to risk-based, AI-ready trials.
wrldOS is built for where the rules now are. ICH E6(R3) Good Clinical Practice — finalized in September 2025 — embraces risk-based quality management and innovations in trial design, conduct, and technology, and the FDA's Guiding Principles of Good AI Practice in Drug Development (January 2026) set expectations for transparent, human-controlled AI. wrldOS keeps AI outputs evidence-linked, audit-trailed, and routed for human review before any regulated decision is finalized.
ICH E6(R3) GCP — final, Sept 2025 · FDA Good AI Practice in Drug Development, Jan 2026Biotech funding collapsed 52%.
Sponsors that treated trial software as discretionary now treat it as material to runway. Projected savings of $700K–$1.9M per trial extend runway by months that matter to whether the program reaches its next milestone.
Funding decline: SVB Healthcare 2024 · BioPharma Dive · Savings range: wrldOS internal modelThe stack is production-ready.
Large language models, mature ensemble OCR, mature anomaly detection, HIPAA-compliant cloud. What required $1M and a specialized team to build in 2020 is developer-accessible and deployable now.
Built by Kelechi Owunwanne — a Clinical Research Associate and operator-founder who spent years across global, mid-size, and specialty CROs. He saw the same structural patterns at every scale, and built what replaces them.
That range matters. Site initiation visits, monitoring visits, close-outs — across multiple operating models, on CTMS, EDC, eTMF, IRT, and safety platforms from every major vendor.
Kelechi on LinkedIn →