Executive Overview

Clinical trials are becoming continuous, sensor-rich, and always-on. That's exciting—and brutal. The volume of signals is exploding while site bandwidth, subject attention, and data quality are still constrained by human workflows.

The winning play is a safety-first agent layer that sits between subjects, sensors, and your trial systems—collecting better data, reducing operational burden, and mediating risk in real time, 24/7. Your competitors are already moving in this direction. The only real choice is whether you buy a generic "everyone-platform" and live inside its constraints, or build proprietary agents that match your protocol, population, endpoints, and risk model—so the system becomes a durable asset, not another vendor dependency.

We work with your team to build the second option.

One critical way we mitigate risk is to architect the system with a human circuit breaker from the beginning. Every AI agent operates within governed boundaries, and when conditions fall outside those boundaries—or when an escalation is triggered—control transfers to human clinical staff. This isn't planning for failure; it's architecting for human invention. In regulated industries like healthcare, this distinction is essential. The system augments human decision-making, but never replaces it. Your clinical team remains the ultimate decision authority, and the architecture ensures that moment of human judgment is preserved, auditable, and designed into every workflow from day one.

We've engineered AI agents in demanding clinical conditions—real-time seizure event detection, sensor-driven behavioral guidance, and safety escalation under strict guardrails—and we've turned those lessons into a configurable framework you can own. We don't sell you a pre-packaged "module." We partner with you to design and build your agents: protocol-specific, compliant by design, fully auditable, and integrated into your data stack.

We build with a workflow-centric methodology that starts from real-world trial workflows—not from technology features. We design for every stakeholder who has to live with the system day to day: research coordinators and CRAs managing operations, principal investigators guiding clinical oversight, and IRB/regulatory teams focused on compliance and trial integrity.

We begin with deep discovery—how work actually gets done, where friction and risk appear, what data must be captured, and what decisions need better support. From that, we craft intuitive experiences and intelligent workflow support that reduce administrative burden, improve data quality, and make adoption feel like relief—not another tool to tolerate.

Only after the user experience is mapped, prototyped, and validated do we lock the technical architecture and implementation plan. That sequence keeps clinical goals in the driver's seat and makes technology the enabler—so teams don't just "use" the system, they recognize it as built for them and celebrate the adoption.

Why Us

• ⚡

  Real-Time Safety & Escalation (Edge + Cloud)

  We've built systems that monitor physiological and behavioral signals continuously, detect anomalies, and trigger structured, rules-governed escalation—without drifting into autonomous clinical decision-making. The agent's job is to capture context, verify events, and route risk to the right human workflow.

• 🧠

  Turning Raw Signals Into Clinical-Grade Context

  Sensors don't produce insight; they produce noise with occasional meaning. We know how to translate device streams into structured, trial-relevant context: symptom timing, triggers, medication adherence clues, cognitive/affective signals, and "what was happening when" metadata that makes downstream analysis far more trustworthy.

• 🛡️

  Guardrails That Keep You in Control

  Every agent operates inside explicit clinical boundaries:

  • • Threshold and trend detection
  • • Protocol-specific question flows
  • • Keyword / intent escalation
  • • Pattern flagging and structured summaries
  • • Complete audit trails (what happened, why, and what was sent)

  As AI improves, your system gets smarter without becoming more autonomous. Control stays with you.

How We Partner: Advise → Enable → Validate → Scale

Your team builds and owns the system. We bring the expertise, patterns, and frameworks to ensure you build it right—faster, safer, and with fewer costly mistakes.

• Advise

  We work alongside your team to translate clinical intent into agent architecture. We help you answer the hard questions before your engineers write a single line:

  • • What data actually drives decisions—and what's noise?
  • • What constitutes a safety signal vs. expected variance for your population?
  • • Where are your non-negotiable rules, escalation pathways, and documentation requirements?
  • • What does "good data" look like for your specific endpoints?

• Enable

  Your engineers build. We guide. We provide the battle-tested patterns, reference architecture, and decision frameworks your team needs to build with confidence—without reinventing what we've already solved in high-stakes clinical environments:

  • • Conversational flow design (structure, branching, failure modes)
  • • Sensor and device integration patterns (real-time ingestion, validation, missingness handling)
  • • Longitudinal memory architecture and context stitching across visits
  • • Intervention and escalation logic (rules-based vs. model-assisted, human handoff design)
  • • Structured output schemas for downstream review and analytics

• Validate

  We review what your team builds against clinical, safety, and regulatory requirements—before it reaches subjects. We bring an independent expert eye to the things that matter most:

  • • Data flow traceability and audit-readiness (EDC / ePRO / eCOA / CTMS connections)
  • • Escalation pathway completeness and edge-case coverage
  • • Immutable logging and governance controls
  • • IRB-facing documentation and SOP alignment support

• Scale

  We help you move from pilot to production without the missteps that derail regulated rollouts. Your team runs the deployment—we advise on how to make it stick:

  • • Pilot design and controlled expansion strategy
  • • Failure mode identification and rollback planning
  • • Staff training frameworks and operational readiness
  • • Ongoing monitoring criteria and configuration governance

• Train

  CROs and trial teams are experts at preparing studies for launch—defining protocols, configuring ePRO systems, setting up data capture rules, mapping workflows. AI agent configuration is a new layer of that same preparation. We train your staff to own it:

  • • How to translate a protocol into agent configuration: thresholds, branching logic, escalation pathways, and safety rules
  • • How to define and manage AI prompts, lexicons, and knowledge collections for each study population
  • • How to configure intervention logic and test it before subjects ever see it
  • • How to stand up a new agent for a new trial—without starting from scratch each time
  • • How to review, audit, and update configurations as protocols evolve

  The goal: your team can spin up a study-ready AI agent the same way they configure an EDC today—methodically, confidently, and without needing outside help.

What You Own

• 🔒 Proprietary AgentsBuilt for your protocol and population. You own the IP, workflows, prompt assets, and integration logic—no licensing traps.
• ⚙️ Purpose-Built ArchitectureDesigned around your endpoints, safety model, and operational reality—not a generic platform you contort into place.
• 📊 Better Data, Faster Response24/7 monitoring, real-time escalation, richer context, fewer missed signals, less subject drop-off, and less site burden.
• 🚀 Future-Proof SystemA framework that can absorb new AI capabilities over time while keeping guardrails and governance intact.
• ⏱️ Fast to Launch6–8 months to production, with regulatory planning alongside build—so deployment is a controlled transition, not a cliff.
• 🧠 The Capability to Build On DemandYour team owns the knowledge and frameworks to configure AI agents for any new study, any new sponsor, any new population—on your own timeline, without external dependencies. That institutional capability becomes yours to keep, refine, and compound across every future trial.

• 🤖 An Expandable Agent Architecture

  We train your team on the two foundational agent types: the study-facing agent that conducts subject interactions, and the intervention agent that monitors it in real time. But the framework is designed to grow. As AI agents become more reliable and your team becomes more confident operating them, new agent types can be layered in—each handling discrete, well-defined tasks with minimal human involvement beyond an approval step. Examples already within reach:

  • • A device logistics agent that receives a replacement signal from the device support agent—once troubleshooting has been exhausted—and handles the fulfillment side: initiating the replacement order and coordinating shipping to the subject, without requiring site staff involvement
  • • An eligibility screening agent that reviews incoming referrals against protocol inclusion/exclusion criteria and surfaces only borderline cases to a coordinator for final review
  • • A protocol deviation detection agent that flags missed visits, incomplete assessments, or out-of-window data in real time—before they become findings in an audit
  • • A data reconciliation agent that cross-checks subject-reported data against sensor readings and EMR feeds, surfacing discrepancies for clinical review

  The architecture you own is not a fixed product—it's a composable framework. Each new agent type you add extends your operational leverage without requiring you to rebuild from scratch.

Part 1: How It Works – The Three-Layer System

Part 2: Configuration Essentials

Every AI agent starts with these four configuration decisions:

Part 3: Agent Types & Templates

Each agent type comes pre-built with default prompts, rules, and workflows. Configure them to your needs:

Part 4: Setting Up Your Data

Your AI agents need three main types of data: sensor data (device readings), knowledge data (protocols, lexicons, guidance), and longitudinal memory (tracking context over time).

Part 5: Safety & Escalation Rules

Safety is built in. You just define the rules that matter to your trial:

Part 6: Cost & Efficiency ROI

Part 7: Real-World Use Cases

Part 8: Unified Agent Architecture

The system uses a streamlined, purpose-built design with two seamlessly integrated agent types:

Part 9: Deployment & Distribution

Deployment in a regulated clinical environment requires careful planning, validation, and compliance review:

Part 10: Technical Architecture

Your existing clinical trial platform with AI agents integrated as a new intelligent interaction layer—a governed agent layer that sits on top of your sensors, systems, and workflows. But here's what makes this different from buying a generic platform: we help you build your own AI Agent Studio. Not a licensed module you live inside—your studio, with your protocols, your prompts, your safety rules, your lexicons, and your agent library. Your team configures and operates it. Your engineers own the integration logic. And every agent you build becomes a reusable asset in a library you control, ready to deploy across studies, sponsors, and populations without starting from scratch. The architecture below shows how the studio assembles these components into governed, task-specific agents—and how the outputs flow back into your platform as structured, audit-ready records.

What an Agent Built in the Studio Looks Like

Every agent your team builds in the studio is assembled from a common set of governed components—prompts, sensors, intervention logic, protocols, lexicons, longitudinal memory, LLM routing, voice, and interactive buttons—all wrapped in rules, protocols, and human oversight. The same configurable building blocks compose any task-specific agent, so your team can launch new agents the same way they configure an EDC today. Outputs flow out through communication pathways (escalations, notifications) and into reporting and compliance (dashboards, IRB), with audit-ready records at every step.

Part 11: Patent Protection & Your Moat

Our technology isn't only an implementation advantage—it's protected ground. MetaBrain Labs' patent family covers the exact architectural shift reshaping clinical trials: sensor-informed, always-on AI agents that interact with subjects, collect structured context, validate signals, and trigger real-time guidance and escalation inside defined safety boundaries.

What the Patent Family Protects

A system that:

• •Uses a digital agent to conduct structured, adaptive interactions (not just passive data capture)
• •Ingests sensor and device signals during the interaction
• •Applies pattern recognition + reference/baseline evaluation to interpret and validate signals
• •Generates structured outputs, interventions, or escalations based on that evaluation
• •Does all this inside a governed architecture with control rules, safety boundaries, and auditability

Why This Matters for You

When you partner with us, you're not just buying development capacity—you're acquiring:

• A Defensible Architecture:Your trial agents are built on a foundation aligned with a protected system design—creating strategic distance from generic platforms racing to bundle "agentic trials."
• Freedom to Build:As more platforms add AI agents, feature parity becomes inevitable. IP is how you avoid becoming one more customer on someone else's roadmap.
• Leverage in Partnerships:A protected system changes the conversation with CROs, sensor partners, and enterprise buyers. You're building an owned capability with defensible differentiation.

Your Moat: Patent + Implementation + Trade Secrets + Configuration

Your competitive advantage comes from:

• •Patent coverage(core system + continuation expansion for newer agent architectures)
• •Implementation know-how(real-world safety monitoring, escalation, subject engagement)
• •Trade-secret assets(agent libraries, rules/guardrails logic, validation pipelines, audit trail design)
• •Your proprietary configuration(protocol-specific triggers, endpoints, escalation pathways, unique to your organization)

Partner Pricing & Exclusive Licensing Options

When you build with us, we offer strategic IP benefits:

• Partner Pricing on Patent Access:Organizations partnering with us receive preferred pricing on patent licensing as part of your partnership agreement—aligning our IP protection with your implementation success.
• Field-of-Use Exclusivity Options:The patent family supports time-limited, scope-limited exclusive licensing—for example, defined exclusivity around a therapeutic area, trial type, or monitoring modality. This transforms IP from "paper" into a competitive barrier that protects your investment and future market position.

Conclusion: You're Building Your Own Internal AI Agents