Why self-host your AI application in a healthcare setting?
Many clinical settings and hospitals have standard operating procedures, or SOPs, and knowledge bases that provide physicians and care providers with guidance when challenging patients arise. Presently, most knowledge bases rely on keyword matching and Levenshtein distance functions to return relevant articles and SOPs. This often forces providers to sift through a large number of documents before finding the information they need.
Large Language Models, or LLMs, offer the potential to drastically reduce the time required to search institutional knowledge bases. However, most implementations depend on third-party providers using general-purpose models. This introduces two significant drawbacks:
For many organizations, relying on large third-party AI providers has become the default. In practice, this often means accepting unnecessary computational overhead, limited control, and environmental costs that scale with model size rather than actual need.
In contrast, smaller, purpose-built, self-hosted AI systems can be designed to do one job well. By limiting scope and tailoring models to real operational requirements, organizations can reduce resource consumption, improve reliability, and maintain direct control over how their systems behave and evolve.
If you are interested in exploring how a more efficient and responsible AI architecture could support your organization’s internal workflows, I can help evaluate existing systems, identify opportunities for right-sizing, and design solutions that prioritize control, efficiency, and long-term sustainability.