# Burna AI

> The safety, tolerability and quality of life platform for clinical research and clinical care. Deepest in oncology, where the grading standard is hardest.

Burna grades adverse events with the evidence attached. The platform produces citation-bound CTCAE grading, MedDRA coding, and attribution decisions for oncology clinical research teams, cancer centers, CROs, and pharmaceutical safety organizations. Twelve specialized agents work in a cascading constraint pipeline so every output carries its source. AI suggests, clinicians decide. Human-in-the-loop, always.

This page is the machine-readable overview for language models. Product pages, audience pages, and long-form essays will expand this corpus as the marketing site grows.

## Principles

- AI suggests, clinicians decide. Every grade, code, and attribution is reviewed by a person.
- Citation-based grading. Outputs are bound to the criterion and source text they came from.
- Full drug lifecycle. The same evidence discipline runs from clinical trials through postmarket pharmacovigilance.

## Products

- [CTCAE grading](https://burna.ai/ctcae-grading): Citation-bound adverse event grading on CTCAE v5 and v6.
- [MedDRA coding](https://burna.ai/meddra-coding): Verbatim terms mapped down the MedDRA hierarchy with codes.
- [Protocol Safe](https://burna.ai/protocol-safe): Attribution decisions made defensible upstream of pharmacovigilance, inside the sponsor's cloud.
- [Postmarket PV](https://burna.ai/postmarket-pv): Safety signal detection and case handling for surveillance.
- [Care Journal](https://burna.ai/care-journal): The patient and family voice between visits.
- [The engine](https://burna.ai/engine): How the cascading constraint pipeline produces evidence-bound output.

## Audiences

- [Pharma and sponsors](https://burna.ai/pharma)
- [Pharmacovigilance](https://burna.ai/pharmacovigilance)
- [Cancer centers](https://burna.ai/cancer-centers)
- [Clinicians](https://burna.ai/clinicians)
- [CROs](https://burna.ai/cros)
- [Patients](https://burna.ai/patients)

## Trust

- [Validation](https://burna.ai/validation): Ongoing internal testing demonstrating strong agreement with expert clinicians.
- [About Burna AI](https://burna.ai/about): Founded and led by Nnenna John, advised by oncologists, pharmaceutical executives, regulatory scientists, informaticists, and operators.
- [Blog](https://burna.ai/blog): Essays on the engine architecture, validation readouts, and industry analysis.
- [FAQs](https://burna.ai/faqs)
- [Contact](https://burna.ai/contact): hello@burna.ai