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85–90% Faster Interim Analysis. 70% Programming Automated. 50% Faster Database Lock. $200–400K Saved.
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Outcomes
What we did
85–90%
Faster interim analysis (3 weeks → 2 days)
70%
Programming effort automated
50%
Faster database lock
$200–400K
Cost savings per trial vs FSP
About Company
A Head of Biometrics leading a US-based team is managing 2–5 concurrent trials, with 70–80% of programming effort spent on SDTM/ADaM mapping and TFL generation.
Interim analyses require 2–3 weeks, creating bottlenecks across trials awaiting statistical outputs.
With $180–220/hr FSP costs and increasing backlog, delays in programming directly impact database lock timelines and submission readiness.
Interim analyses require 2–3 weeks, creating bottlenecks across trials awaiting statistical outputs.
With $180–220/hr FSP costs and increasing backlog, delays in programming directly impact database lock timelines and submission readiness.
Challenges
Key Barriers
to Trial Execution
Programming Backlogs
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80% time on SDTM/ADaM and TFL tasks
Repetitive manual derivations
Delays across study queue
Slow Interim Analysis
2–3 week turnaround times
Manual generation of outputs
Delayed adaptive decisions
Database Lock Pressure
DB lock delays impact submissions
Quality Risk
Manual steps increase error risk
Biometrics team bottleneck
Inconsistencies detected late
Downstream rework required
Downstream regulatory pressure
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
AI Statistical Programming
70% SDTM/ADaM + TFL automation
Validation workflows automated
Shift to high-value statistical work
70% programming effort automated
Automated Interim Analysis
Pipelines generate curves and outputs
Audit-traceable workflows
Weeks of work in 1–2 days
85–90% faster interim analysis
Accelerated Database Lock
Early anomaly detection
Human-in-the-loop validation
Reduced query cycles
50% faster DB lock; $200–400K saved
Maxis AI operated as a governed and supervised execution layer within existing systems throughout.
Core platform
Measured Impact
Quantified Outcomes After Deploying Maxis AI’s Agentic Workflows
| Metric | Before Agentic AI | After Agentic AI |
|---|---|---|
| Interim analysis turnaround | 2–3 weeks of manual statistical programming and data cleaning per cycle | 1–2 days through supervised automated pipeline with expert validation |
| Programming effort | 80% of programmer time spent on repetitive SDTM/ADaM mapping; $180–220/hr FSP rates | 70% automated under supervised execution; programmers refocused on high-value design work |
| Database lock | Delayed in 30–40% of trials; every slip advancing submission risk downstream | Accelerated by up to 50% — $200K–$400K cost savings per trial vs FSP rates |
| Data quality detection | Inconsistencies detected late in manual reviews after downstream rework was already affected | AI flagged anomalies earlier in data flow; 30–40% reduction in data queries |
| Team capacity | Senior programmers consumed by repetitive derivation work with limited capacity for design | 20–30% analytical capacity reclaimed for advanced modelling and adaptive design work |
Outcomes
Interim analysis turnaround compressed from 3 weeks to 1–2 days, database lock accelerated up to 50%, and $200–400K saved per trial vs FSP rates.
85–90% faster interim analysis turnaround (3 weeks → ~2 days)
70% of repetitive programming effort automated under expert oversight
Database lock accelerated up to 50% — $200K–$400K saved per trial vs FSP rates
30–40% reduction in data queries through earlier anomaly detection
2–3× faster decision cycles — adaptive trial decisions made with greater speed and confidence
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