The Award
On 24 February 2024, the Association for the Advancement of Artificial Intelligence (AAAI) recognized our team's neuro-symbolic claims-adjudication engine with the IAAI Deployed Application Award at the Innovative Applications of Artificial Intelligence conference. The award honors AI applications that have moved beyond the lab into sustained, real-world production use — with measurable impact.
Why This Award Matters
The IAAI Deployed Application Award is one of the field's longest-standing recognitions for applied AI. It is not given for prototypes or benchmarks — it requires evidence of an AI system operating in production, at scale, delivering value over time. For insurance leaders evaluating AI vendors, it is a rare independent signal that a system works outside the demo room.
What Was Recognized
The awarded system is the claims-adjudication engine our founding team built and operated at AiDA Technologies: a neuro-symbolic synthesis of language models with a Medical Knowledge Graph, adjudicating health claims in production for major insurers in Singapore and Southeast Asia — at its peak processing over half of Singapore's Integrated MediShield claims.
Production at National Scale
Recognition for AI that adjudicated real claims for real insurers year after year — not a research prototype. The system ran inside the daily operations of some of Asia's largest insurance organizations.
Explainable by Design
Every decision the engine produced was traceable to a policy rule or clinical fact. That explainability is what made the system auditable, regulator-ready, and trusted by the claims teams who worked with it.
The Foundation of Hepha AI
The awarded engine — and the Medical Knowledge Graph at its core — is the foundation on which Hepha AI's claims platform is built. The same team now applies that proven adjudication approach across the full claims pipeline for health and travel lines, serving insurers and TPAs.
Put Award-Winning Adjudication on Your Claims
See what recognized, production-proven AI adjudication looks like on your own claims data.