Aushadhi by Inductive Quotient

Aushadhi is an AI-powered medical coding platform developed by Inductive Quotient, designed to modernize the coding lifecycle across clinical trials and pharmacovigilance workflows. It targets life sciences and healthcare teams that need to process verbatim terms faster, improve coding consistency, and reduce manual effort through intelligent automation and governed review workflows.

The platform uses AI, NLP, and contextual learning to generate high-confidence coding suggestions for MedDRA and WHO Drug dictionaries, while supporting study- and sponsor-specific coding rules. Aushadhi covers the full workflow from verbatim intake through approval, maintaining role-based review, decision traceability, and controlled execution throughout.

AI-Assisted Coding

• Generates coding suggestions for MedDRA and WHO Drug using AI, NLP, and contextual learning
• Produces high-confidence suggestions to reduce variability and minimize rework across studies and programs

Dictionary and Rule Management

• Supports both MedDRA and WHO Drug dictionaries with controlled dictionary governance and version management
• Applies study- and sponsor-specific coding rules automatically to improve consistency and reduce rework

Reviewer Workflows

• Supports role-based review with clear ownership and control
• Enables bulk actions, overrides, and structured approval workflows

Traceability and Compliance

• Maintains audit trails and version control throughout the coding lifecycle
• Tracks decision traceability to support compliant, inspection-ready coding operations

Interoperability

• Connects with EDC, CTMS, and safety systems to streamline verbatim intake, review, and downstream coding workflows
• Supports accelerated review cycles and improved downstream efficiency through system integration

Aushadhi is built to support audit readiness and governed execution across clinical and pharmacovigilance programs, with interoperability across common life sciences systems including EDC, CTMS, and safety platforms.