Solutions · Large CRO
100+ Programs. Margins Protected. No Headcount Growth Required
A global CRO deployed Maxis AI to scale biometrics delivery, save $200K–400K per trial, and establish RBQM without proportional headcount growth.
$200K–400K
Savings per trial
8–12 Wks
RBQM risk advance warning
70%
Programming automation
0 FTE
Additional headcount needed
WHO IT IS
The Context
A global CRO deployed Maxis AI to scale biometrics delivery, save $200K–400K per trial, and establish RBQM without proportional headcount growth.
Headquartered in London, with delivery operations across North America, Europe, and Asia-Pacific, this global CRO was managing a growing portfolio of sponsor programs across oncology, rare diseases, and cardiovascular trials. As new programs scaled, delivery remained dependent on proportional increases in biometrics headcount - limiting profitability despite continued business growth.
At the same time, sponsors were increasingly expecting portfolio-level RBQM. Without a unified data infrastructure, the CRO was unable to deliver a credible, cross-study risk view.
Challenges
Key barriers to Trial Execution
Headcount Model
- Headcount-driven delivery model
- Rising costs with scale
- Shrinking margins
No Portfolio RBQM
- Fragmented data across programs
- No unified risk visibility
- Weak sponsor confidence
Programming Bottleneck
- $180–220/hr FSP costs
- 25–30% attrition
- Quality variability and margin drag
Reactive Quality
- Missed or late risk detection
- 30–40% DB lock delays
- Late escalations
Operational solution
Maxis AI agentic workflows — under human oversight throughout
AI for RBQM
- Unified portfolio risk view
- Risks predicted 8–12 weeks early
Outcome
Portfolio-level RBQM
AI for Programming
- 70% automation
- $200K–400K savings per trial
- Scalable delivery model
Outcome
$200K–400K per trial savings
AI for Data Management
- Query backlog reduced
- Capacity expanded without hiring
Outcome
No FTE expansion required
Maxis AI operates as a governed and supervised execution layer within existing systems throughout.
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Measured impact
Quantified outcomes after deploying Maxis AI's agentic workflows
| Metric | Before Maxis AI | After Maxis AI |
|---|---|---|
| Delivery Margins | Revenue growing; margins eroding - every new study required proportional FTE increases | Delivery capacity scaled without proportional headcount; margins protected at higher volume |
| RBQM Capability | No credible portfolio RBQM view to show sponsors; data fragmented across systems | Unified RBQM portfolio view; predictive signals 8–12 weeks ahead; RBQM as sponsor differentiator |
| Programming Costs | $180–220/hr FSP rates; dataset delivery the margin bottleneck | $200K–400K per-trial savings; programming delivery decoupled from headcount |
| Risk Detection | Reactive; KRI thresholds missing risks until weeks too late | Proactive; risks resolved before milestone impact; sponsor confidence strengthened |
| Scalability | Headcount-constrained - could not bid new programs without margin sacrifice | Scalable delivery; new programs taken on without FTE expansion; RBQM as competitive differentiator |
Outcome
Quantified Benefits
The CRO achieved scalable delivery, restored margins, and established credible RBQM as a competitive differentiator across 100+ programs.
Scaled delivery without added FTEs — higher biometrics throughput
$200K–400K savings per trial vs. FSP rates
Credible RBQM capability — unified risk view with 8–12-week foresight
Stronger sponsor confidence — more wins without headcount growth
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