Funding
Funding not disclosed
Founders
Product
Problem
Large language models frequently generate inaccurate or fabricated information, which is especially risky in healthcare, mental‑wellness, and enterprise decision‑making contexts where incorrect answers can lead to harmful outcomes.
Solution
Born‑AI addresses this risk by integrating ontology‑driven knowledge graphs with generative AI, ensuring that every response is anchored to a verifiable entity such as a SNOMED‑CT code or FDA label. The platform retrieves curated clinical triples and provides citation paths that trace the provenance of each answer, allowing users to see the underlying source material. Uncertain or low‑confidence claims are automatically flagged before they reach clinicians, patients, or business leaders, reducing the likelihood of hallucinations. By combining graph‑grounded retrieval with large‑scale curated data, Born‑AI delivers clinically vetted, traceable outputs for health, mental‑wellness, and enterprise applications.
Target Audience
Primary customers are healthcare providers, mental‑wellness platforms, and enterprise decision‑making tools that require reliable, source‑verified AI assistance.
Features
- Ontology‑driven knowledge graph that maps AI outputs to standardized medical entities (e.g., SNOMED‑CT, FDA labels)
- Retrieval of over 12 million curated clinical triples with provenance metadata
- Automatic citation paths showing source documents for each answer, including FDA label references and peer‑reviewed studies
- Real‑time flagging of low‑confidence or uncertain statements before delivery to end users
- Hybrid quantum‑classical pipelines for high‑dimensional graph embeddings and rare‑disease inference
- API integration for embedding the graph‑grounded AI into existing health‑tech, mental‑wellness, and enterprise systems