Study Lifecycle
AI-Powered Clinical Operations
Automated workflows. Human-guided outcomes.
From Protocol to Build-Ready Assets, Human-in-the-Loop
From Protocol to Build-Ready Assets, Human-in-the-Loop
Study startup delays are a translation problem, not a resourcing one. Every study build team rereads the same protocol and manually produces eCRFs, ALS, and edit checks across separate tools, reconciled by hand. Verify consistency within and across documents, generate traceable startup content, and easily deal with amendments
Platform Performance
Real Results. EDC-Build Ready.
Delays in study build are rarely caused by lack of expertise. They come from manual consolidation and reconciliation across disconnected tools. Study Startup removes that bottleneck while keeping every artefact traceable to its source protocol.
Significant Reduction
Full Traceability
Three Build-Critical Artifacts. One Upload.
Produced together, from the same protocol read, so they never drift apart.
eCRF Forms
Template-based, protocol-aligned form design ready for EDC build.
ALS (Annotated Line Specifications)
Variable mapping traceable to every data element in the study design.
Edit Check Specifications
Range, logic, and cross-form checks drafted directly from protocol requirements.
End-to-End Workflow
From Protocol to Build in Four Steps
A structured, governance-driven framework that compresses weeks of sequential manual work into a single coordinated pass.
Upload the Protocol
Ingest the protocol document mapped to USDM 4.0. Visit schedules, assessments, and data collection requirements are extracted from source text.
Multi-Agent Extraction
Specialized AI agents parse the protocol in parallel, pulling the variables, logic, and rules each artifact needs simultaneously, not sequentially.
Generate Together
eCRFs, ALS, and edit checks are produced together from the same extraction, so all three stay aligned with each other and with the protocol from the start.
Review and Export
Study Build teams review generated assets against source, refine as needed, and export directly into the EDC environment.
From Protocol to Build in Four Steps
Enterprise-grade capabilities designed to combine AI-driven efficiency with governance and control, at every stage of the build.
Multi-Agent Generation
Specialized AI agents work off a single protocol upload to simultaneously produce every build artifact, removing duplicate manual entry across teams.
Protocol-Grounded Consistency
Every generated field, variable, and check traces back to the source protocol, keeping outputs aligned with the intended study design.
Automated Edit Check Definition
Range, logic, and cross-form checks are drafted directly from protocol requirements, catching inconsistencies before they reach the build.
Template-Based Form Design
Protocol-based templates speed up eCRF design while keeping output structured and standards-aligned.
Faster, Parallel Build Cycles
Compresses the traditionally sequential build workflow into a single, coordinated pass.
Who Can Use Study Lifecycle
Purpose-built for end-to-end data teams and decision-makers.
Study Startup is built for the teams who turn a protocol into a working study, and who spend the most time today doing it by hand.
Build assets consistent with the protocol from the first draft, not after three rounds of reconciliation.
Build assets consistent with the protocol from the first draft, not after three rounds of reconciliation.
Stop translating the same document into three separate tools. Generate once, refine once, build.
ALS output that traces every variable to its protocol source, without chasing it manually.
See Study Lifecycle in Action
Experience AI-driven study build that starts with your protocol and ends with EDC-ready assets.