Development, cloud, data and AI for companies
Not every enterprise AI project needs fine-tuning. Most teams that struggle in production chose the wrong adaptation strategy early. This guide gives you the decision framework for RAG vs. fine-tuning, grounded in what each approach changes, where each breaks, and the signals that predict which one your project needs.
Enterprise AI pilots fail at predictable points: no measurable success metric, data that works in a notebook but not in a live pipeline, and no clear owner after go-live. A diagnostic guide for data leaders and CTOs.
AI consulting engagements range from a focused discovery sprint to a full production build. Here is what each phase includes and where the hidden costs appear.
Most "ask your data" demos work once, then break in production. The failure is almost always in the semantic layer, not the AI. Here is the component-by-component architecture of a conversational BI system built on Snowflake Cortex Analyst that actually holds up.
Snowflake charges by compute credit; BigQuery charges by data scanned—or sells you slots. Understanding which model punishes your workload is the decision that drives the bill.
Most AI projects stall not because of bad technology, but because the prerequisites weren't in place. This guide covers the seven conditions your organization should verify before starting its first AI project.