Health Systems
Deliver explainable medication actions directly in clinical workflow.
Deliver explainable medication actions directly in clinical workflow.
Enable labs with DGI signals inside a unified medication decision layer.
Accelerate translational and trial programs with unified medication insights.
Evidence-informed coverage decisions tied to measurable real-world outcomes.
Plain-language medication guidance for safer, better-informed care conversations.
Unified medication-response reasoning with explainable, evidence-linked output.
EHR-native delivery through FHIR/HL7/CDS integration pathways.
Governance, auditability, versioning, and safe rollout controls.
A clear path from pilot to enterprise rollout inside the health system.
Closed-loop Medication Intelligence
PGxAI combines interaction risk, genomic response, and clinical context with real-world outcomes to deliver medication actions in the EHR. The system improves continuously inside the health system with governance and auditability.
Closed-loop Medication Intelligence
Medication response is multi-factorial. PGxAI replaces fragmented checks with one unified response layer across interaction risk, genomic response, and clinical context. It delivers decisions at the point of care and learns from real-world outcomes inside your health system.
Therapy actions with rationale, confidence, and alternatives. Not noisy alerts.
Prioritize high‑impact risks and reduce low‑value interrupts to fight alert fatigue.
Outcome tracking + versioned updates with clinical sign‑off, audit trails, and rollback.
See how it works
Normalize medications, diagnoses, labs, genomics, and context from the EHR and connected systems.
Compute patient‑specific risk and response with evidence‑linked, explainable reasoning and policy controls.
Deliver drug, dose, and monitoring actions with confidence, rationale, and safer alternatives in workflow.
Measure outcomes, evaluate performance, version updates, and safely improve logic inside the health system.
See the Learning Loop
Every recommendation is measurable and versioned. PGxAI uses real-world outcomes to continuously improve decision logic under governance, with audit trails, and without sending sensitive data outside your environment.
Explore Platform
Unified response reasoning across interactions, genomics, and clinical context with evidence links and explainability.
EHR-native delivery via FHIR/HL7/CDS pathways where prescribing happens.
Outcome capture, evaluation, versioning, approvals, and safe releases with audit trails.
Credibility
Partners
InterSystems
Cambridge, MA, USA
Google Cloud
Mountain View, CA, USA
Microsoft
Redmond, WA, USA
NVIDIA
Santa Clara, CA, USA
Sequencing.com
San Diego, CA, USA
Mayo Clinic Platform
Rochester, MN, USA
FDA iSTAND
Silver Spring, MD, USA
The University of British Columbia
Vancouver, BC, Canada
News
PGxAI and Novo Genomics are collaborating to enhance AI-enabled pharmacogenomics in Saudi Arabia, aiming to improve personalized prescribing and medication safety. Their initiative includes local lab processing,…
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PGxAI and Mahd Sports Academy are partnering to bring advanced genetics and AI into athlete development across Saudi Arabia, with the goal of improving personalized training, preventing avoidable injuries, and enhancing…
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PGxAI has announced a strategic partnership with Lean Business Services and Najashi Holding to deploy AI-driven pharmacogenomics across Saudi Arabia in support of the Kingdom’s Vision 2030 Health Sector Transformation…
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PGxAI has been selected as a finalist in the HLTH USA 2025 Startup Pitch Tournament, highlighting the company’s expanding impact in AI-driven precision medicine.
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PGxAI has partnered with Riyadh-based Najashi Holding to bring its AI-powered pharmacogenomics platform to Saudi Arabia.
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PGxAI appoints Roni Zeiger-former Meta Head of Health Strategy, former Google Chief Health Strategist, and co‑founder of Smart Patients-as Product Strategy Advisor to strengthen patient‑centered product development and…
Read moreRequest access to see unified response actions, EHR workflow delivery, and closed-loop learning from real-world outcomes in practice.