Solutions · Site Network
8–12 Week Risk Forecasting, 45% Faster Startup.
Maxis AI helps site networks forecast risks 8-12 weeks early, accelerate startup 45%, reduce queries 60-75%, and prove sponsor-ready oversight.
8–12 Wks
Predictive risk forecasting
Up to 45%
Faster study startup
60–75%
Query reduction
Scalable
Continuous risk oversight
WHO IT IS
The Context
A specialty site network supporting complex clinical studies needed to demonstrate stronger operational performance and quality oversight to sponsors.
The network had patient access and therapeutic experience, but execution was constrained by manual coordination, fragmented site data, and periodic review cycles. Enrollment, monitoring, data quality, and safety signals were not consistently connected across systems.
For sponsors, this created uncertainty. Site performance could not be assessed continuously, risks were escalated late, and operational quality was difficult to prove with clear, audit-ready evidence.
The network needed a governed execution layer that could strengthen sponsor confidence without forcing sites into a heavier manual reporting burden.
Challenges
Key barriers to Site Network Execution
Sponsor Confidence Gap
- Performance was hard to prove
- Quality data was fragmented
- Sponsors needed clearer oversight
Manual Coordination
- Startup relied on manual follow-up
- Activation cycles slowed execution
- Teams chased updates
Limited Risk Visibility
- Data was spread across systems
- Issues surfaced late
- Risks were identified too late
Data Quality Pressure
- Queries consumed bandwidth
- Data issues delayed readiness
- Earlier quality signals were needed
Operational solution
Maxis AI agentic workflows — under human oversight throughout
Sponsor-Ready Site Intelligence
- Unified site, enrollment, data, and safety oversight
- Clear sponsor performance visibility
Outcome
Scalable continuous risk oversight
Predictive RBQM Oversight
- Early risk forecasting across operations
- Adaptive QTL-driven quality management
Outcome
8–12 weeks predictive risk forecasting
AI-Assisted Data Quality Execution
- AI-supported queries and reconciliation
- Earlier issue detection early
Outcome
60–75% query reduction
Maxis AI operates as a governed and supervised execution layer within existing systems throughout.
See it across your site network
Curious how this could improve site performance?
We'll review your execution model, identify operational bottlenecks, and show where governed AI can strengthen oversight in a 30-minute working session.
Measured impact
Quantified outcomes after deploying Maxis AI's agentic workflows
| Metric | Before Maxis AI | After Maxis AI |
|---|---|---|
| Sponsor Quality Evidence | Fragmented reporting | Unified operational view improved sponsor confidence |
| Startup & Activation | Manual startup workflows | Up to 45% faster study startup |
| Risk Oversight | Periodic reviews | Continuous risk oversight |
| Risk Forecasting | Late risk detection | Risks identified 8–12 weeks earlier |
| Query Burden | Manual query management | 60–75% fewer queries |
Outcome
Quantified Benefits
The site network moved from manual, periodic oversight to a sponsor-ready execution model with continuous monitoring, earlier risk forecasting, and stronger operational evidence.
Predictive risk signals surfaced 8–12 weeks in advance
Study startup accelerated by up to 45%
Query burden reduced 60–75% through AI-assisted data quality workflows
Continuous oversight strengthened sponsor confidence
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