NeuroTwin Patient Brain Model
LLM
Neuro-Psych LLM Co-Pilot
Evidence-backed reasoning routed through the POD LLM Gateway.
EVAL
Evaluation & Simulation
Continuous accuracy, calibration and guideline scoring.
SEC
Blockchain Security
Every call policy-checked, every output auditable.
OPS
Clinical Control Plane
Agent identity, data access and workflow monitoring.
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 & structure your data EHR, labs, genomics and IoT, reached in place behind your firewall.
2
Build
Orchestrate agents over your data the POD Master Agent routes work to specialized sub-agents.
3
Prove
Evaluate, simulate and validate accuracy, patient safety and guideline conformance before care.
4
Improve
Learn from real outcomes loop engineering recalibrates the Digital Twin and retrains 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 & 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 & genomics behind your firewall
Trust layer
nspects every layer
Evaluation Agents
Judge & Jury Agents
Governance Agents
Security Agent
Every call auditable
AI Orchestration for Brain Health
AI Agents
Variant-interpretation, drug-response, therapy-ranking, trial-matching and monitoring agents each orchestrating scoped sub-agents up the stack.
Data Sources & Tools
EHR, labs, whole-exome genomics, prior trials and IoT device feeds reached in place via 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 ingests structured and unstructured data natively from Athena, NextGen and Epic, whole-exome PGx (~350 genes / ~800 variants), lab results, prior trials, family history — plus Piper IoT vitals and behavioral signals.
2
Interpret & label
The POD neuro-psych LLM family reads across years of record; annotation agents label every element against a purpose-built neuro-psych ontology — making data AI-workflow-ready and creating 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. The flywheel turns — each cycle compounds the moat.
Why the flywheel is defensible
1
Every patient run improves the underlying engines, which improves the next patient's Digital Twin, which improves the next prediction.
2
The record is recomputed continuously — a view that is current all the time, not a one-time abstraction.
3
Molecular, behavioral and IoT depth that matching-only competitors cannot reach from intake surveys or claims data alone.
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 agent POD Health 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, quarantined and blocked before clinical exposure.
A realtime cockpit 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, the Digital Twin and the full AI Trust Stack behind your firewall establishing reliability, security and accuracy before scale.
The POD Health Thesis