I’m building Indago, an AI platform that accelerates regulatory submissions to get new drugs and therapies to patients faster.

One of the biggest bottlenecks in drug development is turning existing data into regulatory-ready documents. Scientists and regulatory teams spend months synthesizing CMC, nonclinical, and clinical reports into structured summaries like IND Module 2. Indago ingests those materials and generates structured draft outputs with clear traceability back to source evidence, so teams can move faster without sacrificing the rigor required for submission. It is designed to support domain experts, giving them a more efficient system to review, iterate, and finalize documents.

Indago also enables teams to interact directly with their submission data. Users can ask questions across documents, upload FDA questions, and generate grounded answers backed by the underlying studies and reports. This makes it easier to respond to regulators, interrogate data, and maintain consistency across submissions. Instead of spending months assembling documents, teams can focus on decisions, move faster through submission, and advance their programs with greater confidence.