Vigintake offers an AI‑native pharmacovigilance platform that ingests adverse event data from social media, EMR systems, clinical trials and literature into a unified pipeline, translates it in over 100 languages, and automatically applies MedDRA coding, causality assessment and expectedness determination. The system then generates regulatory‑ready case narratives for human‑in‑the‑loop review, enabling biotech, pharma and CRO safety teams to handle global safety intelligence at scale without additional headcount.
Funding
Funding not disclosed
Founders
Product
Problem
Pharmaceutical safety teams face a rapidly growing volume of adverse event reports from diverse sources—including social media, electronic medical records, clinical trials, and literature—making manual intake, translation, coding, and narrative drafting time‑consuming, error‑prone, and costly.
Solution
Vigintake delivers an AI‑native pharmacovigilance platform that automates the entire safety intelligence workflow. The system ingests raw adverse event data from all major channels into a unified pipeline, applies medical‑grade translation in over 100 languages, and uses deterministic AI to perform MedDRA coding, causality assessment, and expectedness determination. It then auto‑generates structured, regulatory‑ready case narratives that maintain clinical consistency across thousands of reports. Each output is presented to safety scientists for a final human‑in‑the‑loop review before submission, ensuring compliance while dramatically reducing manual effort. The platform scales to handle global case volumes without adding headcount, enabling biotech, pharma, CROs, and service providers to meet emerging ICH E2D(R1) requirements for social‑media monitoring.
Target Audience
Primary customers are pharmaceutical manufacturers, biotech companies, contract research organizations, and safety service providers that manage large volumes of adverse event data across multiple jurisdictions.
Features
- Unified intake engine consolidates social media, EMR, clinical trial databases, literature, and patient reports into a single structured workflow
- Medical‑grade translation supporting 100+ languages while preserving clinical terminology and regulatory nuance
- Deterministic AI‑driven extraction that automatically assigns MedDRA codes, assesses causality, and determines expectedness
- Cognitive narrative drafting that generates inspection‑ready case reports with consistent formatting and content
- Human‑in‑the‑loop approval interface allowing safety scientists to review, edit, and sign off AI‑prepared narratives
- Real‑time processing pipeline delivering end‑to‑end case handling from signal detection to submission readiness