Designing systems that replace coordination with execution

Every pharmacogenomic recommendation is only as trustworthy as the evidence beneath it — yet that evidence lives in a dozen incompatible sources, each with its own update cadence, nomenclature, and edge cases. Labs end up stitching CPIC guidance to FDA labeling to variant databases manually, and the seams show. The result is slow turnaround, inconsistent calls, and a review burden that grows with every new gene and drug added to the menu.

The Medication Intelligence Graph fuses every authoritative pharmacogenomic source into one reconciled clinical graph that stands behind each recommendation.

The graph continuously ingests and normalizes CPIC, DPWG, FDA drug labels, PharmGKB, RxNorm, PharmVar, ClinVar, ClinGen, population frequency data, and adverse-event signals — resolving them to a single set of drug–gene relationships with source-level provenance on every edge. SignalAI reconciles conflicts and surfaces the supporting evidence, but it never decides in isolation: your medical director reviews the assembled rationale and owns every clinical call.

One reconciled graph does the work that used to span a dozen systems:

• 50+ genes, 950+ medications, 20+ sources

• Reconciled drug–gene relationships with provenance

• Source-level citations on every recommendation

• Continuous ingestion of guideline updates

Consistency comes from the reconciliation layer, not from individual judgment. When two sources disagree — a label caution against a guideline recommendation — the graph records both, applies a defined precedence, and exposes the conflict for review rather than silently choosing. Every edge carries a citation and a version stamp, so the same genotype and medication yield the same evidence-backed result on Monday as on Friday.

The graph sits underneath your existing reporting and decision-support workflow rather than beside it — no new console for staff to learn. Interpretations, alerts, and report narratives all draw from the same reconciled evidence, and the provenance travels with them into your LIS and downstream integrations, so a reviewer can trace any recommendation back to its exact source in one step.

As guidelines shift and new drugs reach your menu, the graph absorbs the change once and propagates it everywhere — no per-report rework, no stale lookup tables, no divergence between reviewers. Adding genes or medications scales the graph, not your reconciliation headcount, so turnaround and consistency hold steady as volume grows.

Your lab delivers pharmacogenomic reports that are faster to produce and defensible line by line, and your clinicians act on recommendations they can trace to trusted, current evidence.