Solutions · Large Pharma
Portfolio RBQM for 5–300+ Trials. Risks Forecasted 8–12 Weeks Early
Maxis AI supports RBQM oversight for 5-300+ trials, forecasting risks 8-12 weeks early, reducing queries by 60-75%, and speeding DB lock by 30%.
5–300+
Scalable Portfolio Oversight
30%
Faster DB lock
8–12 Wks
Predictive risk forecasting
60–75%
Query Reduction
WHO IT IS
The Context
A large pharma sponsor managing 200+ global trials faced a core execution constraint: coordination across CROs did not translate into consistent execution at scale.
Data signals were fragmented and acted on too late, leading to delayed risk resolution. Database lock timelines slipped by 6–8 months, deferring $3–5M in submission value per study and reducing portfolio predictability.
The issue was not visibility. It was executing on signals early enough to maintain timeline control across a global portfolio.
Challenges
Key barriers to RBQM Portfolio Execution
Fragmented Portfolio Risk View
- Data spread across clinical systems
- No unified risk view across trials and CROs
- Limited leadership visibility
Late Risk Detection
- Static KRIs missed emerging risks
- Issues surfaced weeks or months late
- Actions often came after impact
Query & Data Quality Pressure
- 5,000–50,000 queries per trial
- Heavy manual review burden
- Delayed database lock readiness
RBQM Governance Gap
- RBQM required execution, not just documentation
- Central monitoring varied across programs
- Teams needed adaptive QTLs and audit-ready traceability
Operational solution
Maxis AI agentic workflows — under human oversight throughout
Unified Data Quality Layer
- Unified quality and risk view
- Connected clinical data sources
- Earlier portfolio risk visibility
Outcome
5–300+ concurrent trial oversight
Predictive Risk Signal
- Forecasted emerging risks
- Continuous signal monitoring
- Earlier intervention opportunities-making
Outcome
8–12 weeks predictive risk forecasting
Auto Query Resolution
- AI-assisted query resolution
- Focus on exceptions and review
- Reduced manual query burden
Outcome
60–75% query reduction
Maxis AI operates as a governed and supervised execution layer within existing systems throughout.
See it across your portfolio
Curious how this could work across your trial portfolio?
We'll review your portfolio operations, identify execution bottlenecks, and show where governed AI can improve performance in a 30-minute working session.
Measured impact
Quantified outcomes after deploying Maxis AI's agentic workflows
| Metric | Before Maxis AI | After Maxis AI |
|---|---|---|
| Portfolio Oversight | Fragmented CRO and trial dashboards | Unified RBQM oversight across 5–300+ trials |
| Risk Detection | Static KRIs and delayed reviews | Risks surfaced 8–12 weeks earlier |
| Query Burden | Manual review of 5,000–50,000 queries per trial | 60–75% query reduction |
| Database Lock Readiness | Data-quality issues delayed locks | 30% faster database lock |
| RBQM Execution Model | Manual, fragmented oversight | Continuous RBQM execution with governed monitoring and audit traceability |
Outcome
Quantified Benefits
Clinical leadership gained a unified RBQM execution layer across CROs, trials, and systems, improving early risk visibility, reducing query burden, and strengthening database lock predictability
Portfolio Oversight: Unified RBQM oversight across 5–300+ trials
Risk Detection: Risks surfaced 8–12 weeks earlier
Query Burden: 60–75% query reduction
Database Lock Readiness: 30% faster database lock
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