Go-to-market teams lose most of their time to the wrong list. The data they need sits across several tools, it goes stale within months, and the audience they actually want is usually something a filter cannot express. Landbase removes that friction by combining one large, consistent dataset with an agent you instruct in plain language. This lesson explains the two parts that make it work and why describing what you want produces a better list than clicking through filters.
At its core, Landbase is built on a large go-to-market dataset, which means one structured record of companies and the people who work at them, organized the way a go-to-market team thinks rather than the way a general web crawler stores pages. It spans more than 800 million contacts, over 40 million companies, and more than 1,500 enrichment fields.
Every record follows the same structure, so a given field means the same thing across the whole dataset. That consistency is what lets you target with precision, because you are never left guessing whether two sources defined a value like industry or seniority the same way. When you are unsure whether Landbase holds a particular field, you can ask the agent, since the dataset often covers more than you expect.
On top of the dataset sits an AI agent: you describe what you want in plain English, and it returns a structured list instead of a chat reply.
You never have to learn a query language or memorize filter names, and because the agent keeps improving, your requests return better results over time with no change on your part.
Most data tools give you a search box and a wall of filters. That approach works until your target cannot be expressed as a checkbox, which happens often in real go-to-market work.
For example, you can ask for manufacturing companies with at least three account executives and no SDRs. People make requests like that every day, and no simple filter can express them. Beyond telling you what is true today, the agent helps you decide what to do next.