Blog
Notes on model independence, multimodel orchestration and the governance that decides whether an AI agent is safe to deploy, from the team running the platform inside academic medicine.
Clinicians are building real-looking prototypes faster than product cycles can absorb them. Why that pressures SaaS, and what has to sit between a demo and production.
Read the postGuardrails, validation and monitoring are all necessary. None of them is what decides whether AI takes root inside a hospital.
Read the postWe were making probabilistic text generation trustworthy in hospitals in 2018, before anyone called it GenAI. That environment still shapes how we build.
Read the postNo single model can be trusted on its own. Running the same intent across several at once turns disagreement between them into a signal rather than a silent failure.
Read the postA practical guide to the three enterprise AI agent types, where they fail, and how governance ensures safe deployment in regulated industries.
Read the postWhy enterprise AI strategy is shifting from choosing one model to orchestrating multimodel systems across modalities, LLMs, and hybrid architectures.
Read the postWhy single-model dependency creates long-term enterprise risk, and how model-independent architecture improves resilience, governance, and adaptability.
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