Every LLM demo looks great until it has to read from your CRM, write to your ERP, respect your permission model, and leave a trail an auditor accepts. Closing that gap between demo and deployment is what we do. The agent gets no direct access to your databases or systems. It works only through controlled connections we build, the rules of what it may change are enforced in code, and every interaction is logged, so you always know what the AI did and why. We work the same way we do on the agent systems we run in production, within a sprint whose scope is agreed upfront.

Picture a company that buys sales data, for example from Nielsen: tens of thousands of rows in Excel, dozens of columns, custom category codes, and quirks you simply have to know about. Two people on the analytics team understand them. Every question from marketing or sales, even "how is our new brand doing in discount stores?", joins the queue for those two people, and the answer comes back a week later as a slide deck. In a project like this we build an assistant that knows this data. We move it from Excel into a proper database, describe the structure, the relationships, and the quirks once, in a form the AI understands, and the teams get a chat that answers questions straight from the data: with a number, a table, or a chart. Every answer shows what it was calculated from, so it can be checked. The analytics team stops being the bottleneck for simple questions and gets back to the analyses that genuinely need a human. And the knowledge of how to read this data stops living in two heads.