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60–70% FewerQueries. Faster Lock. $200–400K Saved Per Trial.
Outcomes
What we did
60–70%
Query reduction
40–50%
Faster DB lock
50–70%
Programming timesavings
6–8 Wks
Submission prep
$200–400K
Saved per trial
About Company
Based in SanFrancisco, California, this mid-sized biotech was operating across late-phasestudies under a functional service provider model.
Escalating queryvolumes and programming workload extended projected timelines andincreased operational strain across the biometrics team.
With 5,000–50,000 queries per study and 80% ofdata manager time consumed by manual review, and FSP programmers costing $180–220/hrwith 25–30% annual turnover, the team needed intelligent automation torestore capacity and hit submission deadlines.
Escalating queryvolumes and programming workload extended projected timelines andincreased operational strain across the biometrics team.
With 5,000–50,000 queries per study and 80% ofdata manager time consumed by manual review, and FSP programmers costing $180–220/hrwith 25–30% annual turnover, the team needed intelligent automation torestore capacity and hit submission deadlines.
Challenges
Key Barriers
to Trial Execution
Query Volume
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5,000–50,000 queries per study
80% time spent on manual review
Limited focus on strategic oversight
Programming Load
80% time on repetitiveSDTM/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
6–8 month database lock timelines
3–4 months manual prep for submission
Core platform
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
60–70% query reduction
AI Statistical Programming
50–70% effort reduction Unified data layer across all systems
85% SAP development time reduction
SAS/R/Python supported
85% faster SAP development
Automated Submission Prep
Prep reduced from 3–4 months to 6–8 weeks
AI-assisted documentation & validation
6–8 weeks to submission
Maxis AI operated as a governed and supervised execution layer within existing systems throughout.
Core platform
Measured Impact
Quantified Outcomes After Deploying Maxis AI’sAgentic Workflows
| Metric | Before Agentic AI | After Agentic AI |
|---|---|---|
| Manual Data Effort | ~80% of team capacity on manual review; $850K avg CDM cost per trial; 25–30% annual staff turnover | AI handles 60–70% of routine queries; manual effort reduced 60–70%; 30% cost savings vs. traditional CDM |
| 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 | 6–8 months | 3–4 months (40–50% faster; $3–5M total value with earlier submission) |
| Submission Prep | 3–4 months manual preparation | 6–8 weeks |
| 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 |
Outcomes
Leadership reported stronger confidence in submission readiness and reduced operational strain.Database lock compressed from 6–8 months to 3–4 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
Submission prep shortened from 3–4 months to 6–8 weeks
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