Resolve Data Backlogs Earlier Across Clinical Studies
Clinical data management teams manageincreasing query volumes, but structured resolution across workflows remainscon strained.
Agentic AI for clinical datamanagement enables supervised execution under governance, helping reducebacklog, improve consistency, and support timely database lock.
A Day in
the Life of a CDM Director
You are managing multiple studies withincreasing data volume across sites, systems, and formats. Query backlogscontinue to grow as database lock approaches, while timelines remain fixed.
Your team spends significant time onmanual reconciliation and query resolution, limiting their ability to focus onproactive data quality management. Issues that could have been detected earliernow accumulate close to lock.
The challenge is not dataavailability. It is identifying and resolving issues early enough to preventlate-stage delays.
The Pressures CDM Directors Face
Every Day
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Industry Reality
Clinical trials generate high-volume, multi-source data across EDC, lab, and digital systems
Data inconsistencies across sites increase query burden
Late-stage reconciliation remains a primary cause of database lock delays
Why Maxis AI Is Built for Biometrics
Maxis AI delivers a supervised execution layer—an AI Workforce embedded within CDM workflows.
It continuously monitors incoming data, applies validation rules in real time, and executes defined resolution workflows under governance.
Unlike traditional systems, Maxis AI ensures data issues are not just identified, but resolved early within audit-ready processes.
From Pain to Outcome: How Maxis AI Works for You
| Pain Point | AI Capability | Outcome |
|---|---|---|
| Data errors entering the system undetected, compounding into late-stage reconciliation crises | AI agents apply automated validation rules in real time as data enters, with human-in-the-loop review thresholds for complex issues | 60–70% reduction in manual data queries; database lock achieved 30–40% faster |
| Inconsistent data formats across sites creating integration bottlenecks and coding burden | Supervised intelligent execution maps and normalizes data from EDC, lab, imaging, and device sources within existing systems | 20–30% reduction in data cleaning workload; faster and more reliable data snapshots for interim reviews |
| CDM teams consumed by manual reconciliation with limited capacity for proactive quality management | AI Workforce handles routine cleaning and coding under defined governance controls, freeing data managers for complex exception handling | Faster statistical analysis readiness; improved audit traceability; reduced end-of-study crunch |
All You Need to Know
It monitors incoming trial data continuously, flagging and resolving discrepancies in real time before they become end-of-study reconciliation problems.
Yes. By detecting anomalies at the point of data entry, the AI Workforce reduces manual query volume by 60–70%.
It integrates as a supervised execution layer within your existing EDC, lab, and imaging systems, no core system replacement required.
Continuous data validation throughout the study eliminates the late-stage cleaning backlog that typically delays lock by weeks or months.
Yes. The AI Workforce operates with full audit traceability, human-in-the-loop validation, and regulated workflow alignment.
Looking for Agentic AI for clinical trials?
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