Blog.

What actually raises a restaurant's rating, what the FTC rule bans, and where AI genuinely helps with reviews. Plus the five mistakes owners keep repeating.

Where the loyalty cliches came from, the two effects actually measured in coffee shops, and the only honest way to tell whether your program is working.

MCP became the common language for connecting AI to company systems. Here is what that connection actually costs, where it breaks, and what it means for your data.

Open-weight models have almost caught up with the closed ones, so running your own must be the obvious move by now. It sounds so reasonable that few people check it. What happens when you actually run the numbers?They come out badly for most companies. Against frontier APIs, self-hosting starts w...

Models invent answers because guessing is rewarded, not because they are broken. What that has cost real companies, and the layers production systems use to contain it.

Fine-tuning changes how a model behaves; RAG changes what it knows. Which problem needs which method, what each one costs, and which survives a deletion request? A three-question decision tree.

Beyond the AI agent definitions: a real invoice-processing setup, honest cost ranges, the tools people actually use, and why most agent projects fail.

Building AI infrastructure for your company means stacking four separate management decisions on top of each other: are you building a system that talks to your data, an autonomous agent that carries out tasks, a way to connect your existing systems together, or a decision about running models on...

Writing proposals, chasing follow-ups, and updating the CRM eats a rep's week. Here's how AI speeds it up, and a real legal risk worth knowing about.

Your reviews are a data goldmine nobody's reading. Here's how AI turns theme and sentiment analysis into insight, which tools work, and where they fall short.