GENERIC AGENTIC AI
Architecture-led
- General-purpose model
- Generic knowledge
- Plausible response
- Model confidence
- Opaque processing
STARDEEP™ · EVIDENCE-GROUNDED AGENTIC AI FOR DCTS
Agentic AI for clinical trial.
Grounded in evidence and governed for every decision.
StarDeep transforms NeuTigers' real-world clinical-trial experience, multimodal longitudinal data and operational know-how into specialized AI-agent workflows for decentralized clinical trials.
Outputs are grounded in defined source evidence, checked against protocol and operational rules, independently cross-verified, and escalated for human review when needed.
GROUNDReal-world evidence Multimodal · longitudinal · governed
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ORCHESTRATESpecialized agent swarms Decompose · route · execute
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VERIFYControlled decisions Rules · cross-checks · human review
Five years of controlled trial executionParticipant-level multimodal evidenceIndependent model cross-checkingHuman-accountable escalation
THE INDUSTRY GAP
Agentic AI is easy to demonstrate. Clinical-trial reliability must be proven.
StarDeep is designed around a stricter premise: each operational agent should be evaluated for a defined context of use, against an established reference standard, before it is trusted in a trial workflow.
→ PROOF
STARDEEP™
Evidence-led
- Specialized trial agents
- Clinical + operational grounding
- Evidence-linked output
- Measured task performance
- Inspectable decision trail
EXPERIENCE CONVERTED INTO INTELLIGENCE
Controlled trial execution became a machine-verifiable operating framework.
NeuTigers codifies approved inputs, expected outputs, validation rules and human decision points across the trial pipeline—turning operational know-how into reusable controls for specialized agents.
Protocol design
Study intent, endpoints and operating rules
Input → rule → output → reviewRegulatory preparation
Required evidence, documents and approvals
Input → rule → output → reviewStudy execution
Participants, sites, devices and exceptions
Input → rule → output → reviewData operations
Quality checks, reconciliation and monitoring
Input → rule → output → reviewAlgorithms + apps
Validated features, models and interfaces
Input → rule → output → reviewReporting + audit
Traceable decisions and study outputs
Input → rule → output → reviewSTARDEEP™ EVIDENCE FOUNDATIONParticipant-level linkage Structured for grounding, benchmarking and exception detection
MODALITIES Wearable physiology CGM dynamics Biomarkers Clinical measures
POPULATIONS Risk states Disease phenotypes Demographic diversity Longitudinal outcomes
SETTINGS United States Europe MENA Multiple devices
GOVERNED ARCHITECTURE
Knowledge grounding and verification surround every agent workflow.
StarDeep does not rely on one general-purpose model to manage an entire trial. An accountable supervisor coordinates fit-for-purpose agents and routes material exceptions to human experts.
Trial data + study intent
Protocol · participants · devices · signals
Knowledge + rules layer
Evidence · protocol · terminology · acceptance criteria
StarDeep Supervisor
Understand · decompose · route · monitor
Specialist agent swarms
Validate · analyze · detect · summarize
Governed trial outputs
Dashboards · alerts · reports · audit trail
VERIFICATION GATEWAY
No single model is the final authority.
Source-evidence attribution Deterministic rule checks Data completeness checks Independent model cross-checking Disagreement thresholds Human review + escalation
STARDEEP TRIAL OPERATIONS BENCHMARK
Measured against real trial tasks—not generic AI benchmarks.
Performance is evaluated task by task, for a defined context of use, using historical cases, expert-approved references and agreed acceptance thresholds.
Dimension What is measured Reference
Task accuracy Agreement with expert-approved reference outputs Expert baseline
Evidence fidelity Material statements supported by authorized sources Source attribution
Protocol adherence Correct application of study rules and deviation logic Protocol test set
Safety sensitivity Detection of predefined safety-critical conditions Safety cases
Escalation quality Appropriate escalation with controlled false-alert burden Human adjudication
Operational efficiency Cycle time, correction burden and cost per completed task Current workflow
Proof status: Site claims should move from pilot objective to demonstrated outcome only after results are measured, reviewed and documented for the specified context of use.
OPERATIONAL VALUE
From manual coordination to continuously governed trial operations.
StarDeep pilots are designed to quantify improvements without transferring accountability away from sponsors, investigators or clinical experts.
Shorter review cycles
Measure time from incoming trial data to a validated operational output.
Earlier exceptions
Measure detection of missing, inconsistent or anomalous information.
Parallel execution
Coordinate specialized tasks while maintaining dependencies and oversight.
Decision provenance
Reconstruct the evidence, rule, model, reviewer and action behind each output.
VALIDATE BEFORE YOU SCALE
Design a StarDeep™ validation pilot.
Establish the human baseline, replay historical cases, operate in shadow mode and compare quality, safety, time and cost before broader deployment.
Define
Select the workflow, context of use and acceptance thresholds.
Baseline
Measure current human performance, effort and cycle time.
Replay
Test historical cases withheld from the active agent workflow.
Shadow
Run alongside the live process with no autonomous authority.
Decide
Compare quality, safety, time and cost before scale-up.
StarDeep™ is an investigational operational platform. Capabilities, validation requirements and human oversight should be defined for each intended context of use. References to regulatory frameworks do not constitute certification or regulatory authorization.