blog/Blog
Teams default to LLMs for everything now. But for classification, ranking, and prediction tasks with labeled data, classical ML is often faster, cheaper, and more reliable.
Retrieval quality is the bottleneck nobody talks about. Chunking strategies, embedding model choice, and the hybrid search trick that changed everything for our pipeline.
A decision framework for health tech CTOs evaluating whether to build internal AI capabilities or integrate third-party solutions. Spoiler: it depends on your data moat.
Model Context Protocol is powerful but the ecosystem is young. How I'm using MCP for HubSpot, Google Workspace, and custom data sources — and what broke along the way.
Most teams reaching for autonomous agents should be using structured function calling instead. It's more predictable, cheaper, and easier to debug. Here's when each approach wins.