Command Center
One place to see workload, urgent review items, source health, and next actions.
Pharmacovigilance workflow platform
The AI command center for PV teams
Apexity brings literature monitoring, signal context, ICSR intake, label review, and audit trails into one evidence-linked safety workspace.
PV command centerLive safety intelligence
Literature monitor
EvidenceProduct-event evidence ready for triage
Case inbox
TriageICSR candidate summary and source context
Listedness review
DecideLabel context, expectedness support, decision log
Evidence linkedQueue routingAudit trail
Why it matters
Stop stitching together safety evidence by hand.
Literature, FAERS, labels, cases, signals, and reports often live in separate tools and queues. Teams lose time reconciling source provenance, duplicate records, evidence snippets, and reviewer decisions.
Apexity gives safety leaders one operating layer for source-grounded review and controlled handoffs.
Product
One workspace for the PV operating loop
Apexity turns source monitoring, evidence extraction, reviewer routing, and audit-ready outputs into connected workflows.
Case Inbox
Review source-derived ICSR candidates before promotion into operational case workflows.
Literature Monitor
Monitor PubMed, Europe PMC, PMC OA, and manual uploads with review queues and source-to-case extraction.
Signal Dashboard
Review public signal context, disproportionality views, supporting evidence, and caveats around spontaneous reports.
Labels & Listedness
Compare labels, support expectedness review, and capture reviewer decisions with source-backed context.
Reporting & Audit
Create line listings, review records, exports, governance views, and traceability for audit preparation.
How it works
Turn fragmented sources into review-ready work
1
Ingest sources
Bring in literature, labels, selected public safety context, and manual uploads.
2
Normalize and dedupe
Structure records, reconcile duplicates, and maintain source provenance.
3
Score and extract evidence
Surface product-event matches, evidence spans, ICSR likelihood, and reviewer explanations.
4
Route for human review
Queue items to the right reviewer with disposition, comments, and role-based actions.
5
Create outputs
Generate traceable review records, line listings, handoff packages, and audit trails.
Solutions
Built around the work safety leaders already manage
For PV Operations
Intake, triage, queue management, SLA visibility, and handoffs.
For Medical Safety
Evidence review, ICSR likelihood, medical assessment support, and signal context.
For Regulatory / Labeling
Listedness workflows, label comparison, and source-backed decisions.
For QA / Compliance
Audit trails, reviewer actions, provenance, and controlled workflows.
For Literature Surveillance
Scheduled monitoring, live search, full-text resolution when legally loaded, manual upload fallback, and deduplication.
Platform
The workflow layer between sources and safety decisions
Apexity connects source monitoring, AI-assisted evidence extraction, reviewer routing, and governance controls without replacing PV judgment.
PubMed, Europe PMC, PMC OA, and manual uploads.
Product/event matching, evidence spans, ICSR likelihood, and reviewer explanations.
Queue routing, disposition, candidate creation, and case handoff.
Human review, audit history, role-based actions, and source provenance.
Governance
Every safety decision, backed by evidence.
Apexity keeps review work structured from intake to disposition, so teams can move faster without losing traceability.
Reviewer workbench
Prioritized queues, evidence context, and clean disposition flows.
Evidence traceability
Outputs stay linked to source records, evidence spans, and provenance.
Audit-ready records
Actions, comments, decisions, and handoffs are captured as work happens.
Decision recordReady for QA
SourceLiterature Monitor
Evidence3 linked excerpts
DispositionReviewer accepted
HandoffCase inbox
Proud to Work With
Trusted by leading pharmaceutical companies worldwide
Company
Building the workflow layer for modern drug safety
Founder & CEO
Henny Kim
Columbia University - Computer Science & Mathematics
Experience
Software Engineer at TikTok
Quantitative Developer at Blackstone
Quantitative Research at Morgan Stanley
AI Developer at Hanwha Investments
Chief Technology Officer
Jonathan Cui
University of Waterloo - Computer Science
Experience
Software Engineer at Citadel
Software Engineering Intern at Databricks
Software Engineering Intern at LinkedIn
Software Engineering Intern at Snowflake
See Apexity on a real PV workflow
Open the Celltrion pilot workflow for literature surveillance, signal review, case intake, and label/listedness workflows.
See a Demo