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  • Study Startup

    Budget Share:
    10-15%
    As-Is:
    $5–7.5M
    To-Be:
    $2–3.5M
     
    60% Startup Cycle Compression

  • Study Startup

    Budget Share:

    10-15%

    As-Is:

    $5–7.5M

    To-Be:

    $2–3.5M
     
    60% Startup Cycle Compression

  • Study Startup

    Budget Share:

    10-15%

    As-Is:

    $5–7.5M

    To-Be:

    $2–3.5M
     
    60% Startup Cycle Compression

  • Study Startup

    Budget Share:

    10-15%

    As-Is:

    $5–7.5M

    To-Be:

    $2–3.5M
     
    60% Startup Cycle Compression

  • Study Startup

    Budget Share:

    10-15%

    As-Is:

    $5–7.5M

    To-Be:

    $2–3.5M
     
    60% Startup Cycle Compression

  • Study Startup

    Budget Share:

    10-15%

    As-Is:

    $5–7.5M

    To-Be:

    $2–3.5M
     
    60% Startup Cycle Compression

The Saama Difference

Saama has over a decade of experience developing and training AI models for life sciences that can be used out-of-the-box. Other companies are just beginning this research. By partnering with Saama, you are ahead of the curve.

Saama has its own AI research lab that consists of some of the world’s best AI researchers globally.
In-House Dedicated AI Researchers — our lab has spent a decade building, training and evolving AI/ML models specifically for life sciences.

19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
19 +
Trained AI/ML Models

Our AI research lab has spent a decade building, training and evolving AI/ML models specifically 

 
Study Startup
A single, real-time view of study, country, and site-level start-up activities with automatically tracked milestones, surfacing bottlenecks before they delay first-patient-in
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Clinical Data Management
Automated data review, cleaning, and reconciliation that flags discrepancies and generates queries as data is collected, keeping the database clean through to lock
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Clin Ops & CRA
Role-specific, real-time visibility across operational, clinical, and financial sources, so teams can prioritize site activity and act on risks instead of chasing reports.
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Next-Gen RBQM
Continuous monitoring of data and processes across the trial, with predictive risk assessments and recommendations that de-risk operationsin real time.
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Medical/Safety
Centralized patient data with real-time AI that predicts patient behavior, tracks progress, and accelerates clinical signal discovery.
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Biostatistics & Submissions
AI-automated dataset transformation and mapping that simplifies and accelerates regulatory submissions.
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Clinical Trial Transformation

Transforming the FPI-to-Submission corridor with AI & Automation across all five functional domains of clinical trials

$15–21M

Per-Trial AI Savings

70%

LPLV to Submission Compression

35–45%

Productivity Gain

$20–30M

Per-Trial AI Savings

90%

LPLV to Submission Compression

45–65%

Productivity Gain

Why Clinical Trials Become More Expensive Than Planned

The Hidden Cost Between First Patient In and Submission

How Saama Uses AI and Automation to Compress the FPI-to-Submission Timeline

Six domains, one compressible cost structure. A typical Phase II costs $7–20M, with per-patient costs averaging $36K–$48K.

Most Trial Costs Are Controllable

50–70% of study costs come from five operational areas. 60–75% of the trial budget sits within 6 optimizable domains (excluding unavoidable passthroughs). For a $15M Phase II, that’s $9–11.25M in compressible spend from study startup through submission.

Time-to-Submission Delays

Months Lost Between Last Patient Visit and Submission. LPLV-to-Submission averages 20-24 weeks – LPLV is not as intuitive.

Biostatistics & Submission Bottleneck

Sequential manual processes add 8–12 weeks post-lock, with 30–40% rework rates that delay submissions and consume valuable resources.

Manual Processes Are Straining Clinical Teams

Clinical teams are being asked to do more with less. Manual data review, repetitive monitoring activities, and document-heavy workflows increase operational burden and contribute to a 54–67% error rate. At the same time, CRA turnover is at 22%, while AI volume is growing 20% annually.

Slow Trial Start-up

Country submissions, ICFs, IBs and site activation packages average 6–9 months — delaying first patient in and burning 10–15% of budget before enrollment.

Protocol Complexity Drives Avoidable Amendments

Poorly optimized protocols increase study costs, startup delays, site burden, and amendment risk. Early optimization can prevent costly downstream rework.

TOTAL ADDRESSABLE SPEND

$25.5–37.5M

per Phase III trial

51–75% of total budget across five functional domains

Clinical Trial Transformation

Transforming the FPI-to-Submission corridor with AI & Automation across all five functional domains of clinical trials

$15M–$21M

Per-Trial AI Savings

70%

LPLV to Submission Compression

35%–45%

Productivity Gain

$20M–$30M

Per-Trial AI Savings

90%

LPLV to Submission Compression

45%–65%

Productivity Gain

Why Clinical Trials Become More Expensive Than Planned

The Hidden Cost Between First Patient In and Submission

How Saama Uses AI and Automation to Compress the FPI-to-Submission Timeline

Cost Control

Most Trial Costs Are
Controllable

50%–70% of trial spend is concentrated in five optimizable areas — yet most sponsors treat it as fixed.

Saama clients recover 15%–25% of compressible spend by applying AI-driven automation across data review, monitoring, and submission workflows — without touching fixed passthroughs.

Submission Delay

Time-to-Submission
Bottleneck

LPLV-to-submission averages 20–24 weeks. Every extra week is budget burned and a patient waiting.

Automated data cleaning, lock readiness dashboards, and continuous signal detection compress the LPLV-to-lock window by 6–8 weeks — getting submissions out faster and teams off-trial sooner.

Rework Cycle

Biostatistics & Submission Rework

8–12 weeks post-lock, 30%–40% rework — because quality checks happen too late in the process.

Continuous quality checks running throughout the trial — not just at lock — reduce post-lock rework by up to 60% and cut statistical review cycles from weeks to days.

Staff Burden

Manual Processes Strain Clinical Teams

CRA turnover at 22%. AI workload growing 20% annually. The math doesn’t work without automation.

Saama’s AI agents automate up to 70% of routine monitoring tasks — SDV, source data review, query management — so CRAs spend time on sites, not screens. Lower burden, lower attrition.

Start-Up Loss

Slow Trial Start-Up Burns
Budget

6–9 months to first patient. 10%–15% of budget spent before a single data point is collected.

Digitized protocol-to-site packages and AI-assisted regulatory submission cut start-up timelines by 8–12 weeks, preserving $1M–$3M in pre-enrollment budget on a typical Phase II.

Protocol Waste

Protocol Complexity Drives Avoidable Amendments

Most amendments are preventable. Each one costs $450K–$900K and disrupts every active site.

Protocol optimization at design time reduces amendment rate by 30%–40% — eliminating an average of $450K–$900K in per-amendment costs and the site disruption that follows.