One agentic platform: it unifies the patient record, simulates the best-fit therapy before a single dose,, and secures every output behind your firewall. Compliance, Governance and Evaluation agents validate each recommendation, so it reaches care explainable, traceable and governed.
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Five layers, from the EHR and lab data foundation to the clinical agents oncologists and patients use, with evaluation, governance and security agents inspecting every one.
The gSage agentic stack coordinates every agent, tool and data source so the whole system reasons as one, moving cancer AI from decision support to decision intelligence.
Deep Research, Digital Twin, Monitoring and Pre-Auth master agents, each orchestrating scoped sub-agents up the stack.
EHR, labs, genomics, imaging and devices, reached in place via governed MCP servers behind the firewall.
Retrieval over the cancer oncology and patient-graph models brings the right context to every decision.
Input, reasoning, treatment, outcome, recalibration. Each closed loop makes the next recommendation sharper.
Data flows continuously from EHRs, labs and cancer repositories into the agentic layers, and because each pass derives new structured data, the record deepens and the engines improve with every patient.
Calls variants, derives signatures (HRD, MSI), computes pathway-level scores against curated knowledge bases.
The OncoLLM family reads across years of record; annotation agents label every element against a purpose-built cancer ontology, making the data AI-workflow-ready.
The unified, cited record feeds the Digital Twin, Deep Research, Monitoring and Pre-Auth agents, clinical, administrative and research workflows all reading from one source of truth.
Live ctDNA and IoT data plus realized outcomes flow back to recalibrate the twin and retrain the engines.
Evaluation agents score every output against ground truth and guidelines on live dashboards, while the GeneSilico AI Agent Simulator proves each response through a full rules-and-safety gauntlet before it reaches care.
Conforms to NCCN / ASCO guidelines for this tumor and line.
Safe for this patient: comorbidities, consent.
Holds up against real outcomes, calibrated.
Every access in-policy, free of injection or tampering.
Live accuracy dashboards by tumor type, model and workflow, stratified across subgroups to surface disparities.
Realized outcomes and reviewer feedback return to the engines; reliability improves with every case the platform sees.
Independent agents cross-examine every output against cited evidence and each other, catching unsupported content before it reaches care.
Checked against HIPAA, HL7/FHIR and regulatory obligations, built into the architecture, not asserted after.
Corroborated against NCCN, ASCO and cited source evidence, so what surfaces is grounded and defensible
Each provider's own policies and formularies are enforced, so outputs match how this institution practices
Individual clinician biases are surfaced and checked, keeping recommendations equitable.
A live control plane that governs how every agent runs, tracking identity, enforcing data access by policy, and stopping the adversarial attacks that target agentic systems handling patient data.
A real-time view of how physicians and AI agents work together, how outputs perform for each patient, and where clinical workflows bottleneck, so administrators can act on it.
Accept, edit or override, tracked per patient
Output quality by tumor type, model and workflow
Find where workflows stall, and fix them
A typical 60–90 day pilot stands up Casebook, the Cancer Digital Twin and the full AI Trust Stack behind your firewall establishing reliability, security and accuracy before scale.