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In the early stages of generative AI adoption, many teams focused on model performance alone. However, as we move into 2026, the industry has realized that the surrounding infrastructure- the "Ops" in LLMOps - is actually what determines the success of an AI initiative.  LLMOps Development Services  provide the engineering discipline needed to manage LLMs like any other essential software component, with versioning, testing, and continuous monitoring. Let’s break down what LL

Physical therapy clinics operate with workflows that standard healthcare software was never designed to handle. Treatment plans span weeks or months, progress tracking is continuous, exercise adherence happens outside the clinic, and billing involves therapy-specific codes that general systems struggle with.  Building software that truly supports these workflows  means designing around day-to-day therapy routines, patient progress tracking, and the coordination between therap

Operations teams have long used automation to reduce repetitive tasks. Early approaches relied on scripts, then workflows, and later more configurable platforms. These systems improve efficiency, but they typically follow predefined sequences and need human oversight when conditions change. Multi-step AI agents  take a different approach. They receive an operational goal and work through the necessary steps to complete it, maintaining context and checking outcomes along the w

When software controls an infusion pump, analyzes a diagnostic image, or triggers a patient alert, the engineering rules change. A software error is not just a support ticket - it’s a clinical event that can lead to patient harm, trigger a recall, or prompt regulatory action. That reality influences every aspect of how medical device software is built. Requirements tie directly to clinical needs, not feature requests. Testing verifies safety, not just functionality. And every