FSP BIOMETRICS SERVICES
60–70% Fewer Queries. Faster Lock. Risks Forecasted 8–12 Weeks Early
Maxis AI reduced queries 60-70%, accelerated DB lock 40 - 50%, cut programming effort 50 - 70% and forecasted risks 8 - 12 weeks early.
60–70%
Query reduction
40–50%
Faster DB lock
50–70%
Programming time savings
8-12
Wks Predictive risk forecasting
$200K–400K
Saved per trial
WHO IT IS
The Context
Based in San Francisco, California, this mid-sized biotech was operating across late-phase studies under a functional service provider model
Escalating query volumes and programming workload extended projected timelines and increased operational strain across the biometrics team
With 5,000–50,000 queries per study and 80% of data manager time consumed by manual review, and FSP programmers costing $180–220/hr with 25–30% annual turnover, the team needed intelligent automation to restore capacity and hit submission deadlines
Challenges
Key barriers to Trial Execution
Query Volume
- 5,000–50,000 queries per study
- 80% time spent on manual review
- Limited focus on strategic oversight
Programming Load
- 80% time on repetitive SDTM/ADaM mapping
- $180–220/hr FSP cost
- 25–30% annual turnover
SAP Timelines
- 4–8 week SAP development cycles
- Delays in trial readiness
- SAS-to-Python/R transition burden
DB Lock Delays
- 15–18 months database lock timelines
- 3–4 months manual prep for submission
Operational solution
Maxis AI agentic workflows — under human oversight throughout
AI Data Management
- 60–70% queries auto-resolved
- Human oversight maintained
- Shift to strategic focus
Outcome
60–70% query reduction
AI Statistical Programming
- 50–70% effort reduction
- 85% SAP development time reduction
- SAS/R/Python supported
Outcome
85% faster SAP development
Predictive Risk Forecasting
- Earlier data quality risk detection
- Forecasted query
- Risks surfaced
Outcome
8–12 weeks predictive risk forecasting
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 |
|---|---|---|
| Manual Data Effort | 80% manual review; $850K avg CDM cost; 25–30% turnover | 60–70% routine queries handled; 30% CDM cost savings |
| Programming Workload | 80% of programmer time on SDTM/ADaM mapping; $180–220/hr FSP rates | 50–70% automated; 85% SAP development time reduction; SAS/R/Python supported |
| Database Lock | 15–18 months | 9–12 months (40–50% faster; $3–5M total value with earlier submission) |
| Predictive Risk Visibility | Risks surfaced late through manual reviews | Risks forecasted 8–12 weeks earlier |
| Cost Per Trial | Traditional FSP: $180–220/hr; $850K avg CDM cost per trial | $200K–400K direct savings; 20–40% lower cost vs. FSP; $3–5M total value with earlier submission |
Outcome
Quantified Benefits
Leadership reported stronger confidence in submission readiness and reduced operational strain. Database lock compressed from 15–18 months to 9–12 months.
AI handled 60–70% of routine data queries under human oversight
Statistical programming effort reduced 50–70%
$200K–400K saved per trial vs. traditional FSP rates
Predictive risk signals surfaced 8–12 weeks in advance
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