Your coding backlog is not a staffing problem anymore. It is a structural one. Hospitals are sitting on 10 to 45 day chart backlogs. Practices are one sick coder away from a frozen cash flow. RCM companies are quoting lower fees…
LLM-Native Application Development
Applications built from the ground up around large language model capabilities
LLM-native applications are not traditional software with an AI API bolted on. They are designed from the first architecture session around what LLMs do well — language understanding, generation, reasoning, and retrieval — with UI, workflows, data models, and backend services all shaped by how the AI layer works, not the other way around.
- Conversational enterprise applications — natural language interfaces to ERP, EHR, CRM, and analytics platforms that understand context, complete tasks, and learn preferences
- RAG-powered knowledge applications — enterprise knowledge bases, document Q&A systems, and policy search tools grounded in your documents with cited, accurate answers
- LLM workflow orchestration — multi-step reasoning applications that decompose complex tasks, use tools, and produce structured outputs at each stage
- Prompt engineering and management systems — versioned prompt libraries, A/B testing infrastructure, and regression testing to maintain LLM output quality over time
- Multi-modal AI applications — applications that process text, images, documents, and audio together — clinical notes with lab images, invoices with tables, reports with charts
- LLM evaluation and quality pipelines — automated accuracy, groundedness, and safety scoring embedded in every deployment pipeline to catch regressions before users do
