NeuroTwin Patient Brain Model
OPS
Clinical Control Plane
Agent identity, data access, and workflow monitoring.
EVAL
Evaluation & Simulation
Continuous accuracy, calibration, and guideline scoring.
SEC
Security Control Plane
Every call is policy-checked and every output is auditable.
LLM
Neuro-Psych LLM
Evidence-backed reasoning routed through the POD LLM Gateway.
5 layers
one governed AI stack
80%+
AI accuracy + Agentic RAG
NeuroTwin
on every patient decision
Firewall
blockchain-secured deployment
The AI Agent Lifecycle
One governed loop. From your data to trusted decisions.
Every brain-care recommendation runs the same four stages, with the POD AI Trust Stack inspecting all of them so nothing reaches a clinician unvalidated, and every outcome makes the next decision sharper.
1
Connect
Ingest and structure your data, including EHRs, labs, genomics, and IOT devices securely behind your paywall.
2
Build
Coordinate AI agents across your data. The POD Master Agent routes work to specialized sub-agents best equipped to handle it.
3
Prove
Evaluate and simulate care before it happens. Validate its accuracy, patient safety and conformance with guidelines.
4
Improve
Real-world outcomes feed back into the system, helping recalibrate the Digital Twin and retrain the engines.
The POD AI Trust Stack
Runs across every stage. Nothing reaches a clinician without passing through it.
Experience Agents
Deliver AI to clinicians, patients, and families.
▲ orchestrated by ▲
Master Orchestration Agent
Routes work to specialized agents
NeuroPrecision Dx
OpenMind Co-Pilot
Piper Companion
Trial-Match
Billing Access
▲ reads & writes ▲
CaseVault Agent
Maintains one unified patient record
▲ draws on ▲
Model Gateway Agent
Routes to governed best-of-breed models
▲ sources from ▲
Data Connector Agent
Reaches EHR, labs, and genomics behind your firewall.
Trust layer
Inspects every layer
Evaluation Agents
Judge & Jury Agents
Governance Agents
Security Agent
Every call is auditable.
AI Orchestration for Brain Health
AI Agents
Variant-interpretation, drug-response, therapy-ranking, trial-matching, and monitoring agents each coordinating scoped sub-agents up the stack.
Data Sources & Tools
EHR, labs, whole-exome genomics, prior trials, and IoT device feeds are accessed in place through governed MCP servers behind the firewall.
RAG + Knowledge Graphs
Graph + Agentic RAG over the neuro-psych ontology and patient-graph models drives POD AI accuracy beyond 80% and grounds every decision.
Loop Engineering
Genomics → reasoning → treatment → outcome → recalibration. Each closed loop makes the next recommendation sharper.
The Data Platform
1
Ingest from every source
POD natively ingests structured and unstructured data from Athena, NextGen, and Epic, including whole-exome PGx data (spanning ~350 genes and ~800 variants), lab results, prior trials, family history, Piper IoT vitals, and behavioral signals.
2
Interpret & label
The POD neuro-psych LLM family reads across years of patient records, while annotation agents classify each element using a purpose-built neuro-psych ontology. This makes the data ready for AI workflows and creates new structured variables.
3
Drive multi-agent workflows
The unified, cited CaseVault feeds the Digital Twin, OpenMind, trial-matching, and monitoring agents clinical, administrative, and research workflows all reading from one source of truth.
4
Learn from outcomes
Piper IoT signals and realized outcomes flow back to recalibrate the Digital Twin and retrain the engines. With each cycle, new data and outcomes improve the platform, strengthening its advantage over time.
Why the flywheel is defensible
1
Every patient runs improves the underlying engines, strengthening the next patient's Digital Twin and improving the next prediction.
2
The record is recomputed continuously. This creates a view that is always current, rather than a one-time abstraction.
3
Molecular, behavioral, and IOT data provide depth that competitors relying only on intake surveys or claims data cannot match.
Evaluation & Continuous AI Training
Neuro-Psych Rules
Does the response conform to neurology and psychiatry clinical rules and the evidence guideline base for this condition?
Patient Compliance
Will the recommendation be feasible for this patient’s care plan, risk profile, family context, and adherence pattern?
Treatment Accuracy
Does the plan align with genomics, current medications, prior trials, phenotype, and expected response trajectory?
Security
Is the output compliant with agent policy, data-access scope, privacy requirements, and adversarial attack defenses?
Continuous evaluation
Live scoring on accuracy, calibration, guideline adherence, and agreement with ground truth.
Simulate before deploy
Responses are tested against clinical goals before they reach clinicians, patients, or families.
Continuous training
Evaluation data feeds back into agent policy, loop engineering, and model improvement workflows.
Compliance
HIPAA and runtime policy checks before output exposure.
Guideline Adherence
Clinical rule and evidence-base scoring for every response.
Clinical Policy
Health-system specific restrictions and escalation requirements.
Bias Detection
Equity and safety checks across recommendations and access paths.
Continuous Operations & Security
Agent Identity & Registration
Every POD Health agent or registered third party must be registered and carry a runtime-enforced policy contract.
Blockchain Records & Data Access
Immutable decision traceability and policy-scoped data access keep every clinical recommendation auditable.
Attack Defense
Adversarial prompts, policy breaches, and non-compliant tools are detected before they can reach clinical workflows. Any identified threats are quarantined and blocked to prevent clinical exposure.
A realtime dashboard for clinician + AI collaboration, throughput, accuracy, and bottleneck resolution.
Clinician + AI collaboration
Throughput & accuracy
Bottleneck resolution
A typical 60-90 day pilot stands up the CaseVault, Digital Twin, and full AI Trust Stack behind your paywall. This establishes reliability, security, and accuracy before scaling.
The POD Health Thesis