Perceptic offers an AI‑driven operating system that unifies evidence, data, and decision workflows across the entire drug development lifecycle. By providing a shared intelligence layer that connects asset scouting, scientific evaluation, and clinical data, the platform enables transparent, traceable decisions and lets insights from any team compound to inform every choice. It extends existing scientific infrastructure with continuous learning to ensure each call is fully backed by the organization’s collective knowledge.
Funding
Funding not disclosed
Founders
Product
Problem
Drug development teams struggle to integrate disparate evidence, datasets, and decision processes across scouting, scientific evaluation, and clinical stages, leading to fragmented insights and opaque decision trails.
Solution
Perceptic delivers an AI-driven operating system that serves as a unified intelligence layer for the entire drug development lifecycle. By connecting evidence, data, and reasoning across teams and tools, the platform enables every insight to compound and supports transparent, traceable decision workflows. The system extends existing scientific infrastructure, continuously learns from user interactions, and ensures that each decision is grounded in the organization’s full knowledge base.
Target Audience
Primary customers are pharmaceutical R&D organizations and biotech companies that need to coordinate multi‑disciplinary drug development efforts from discovery through clinical phases.
Features
- Centralized knowledge graph that links asset scouting data, preclinical findings, and clinical trial results in a single searchable repository
- AI-powered inference engine that surfaces relevant evidence and suggests data-backed hypotheses during evaluation
- Traceable decision workflow module that records approvals, rationales, and supporting data for auditability
- Integration adapters for common laboratory information management systems (LIMS), electronic lab notebooks (ELN), and clinical data platforms
- Real-time collaboration tools that allow cross-functional teams to annotate, comment, and co‑author analyses within the platform
- Continuous learning loop that refines relevance models based on user feedback and outcomes