What Landbase is, in 90 seconds

Overview

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 natural 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.

The dataset

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.

What is inside

• Company records carry firmographics such as industry, size, revenue, location, tech stack, and funding.
• People records add role detail, including title, department, management level, location, and tenure.
• Signals show what is changing at an account, such as hiring, funding, tech-stack shifts, and market activity.

Why consistency matters

Landbase uses one standard set of labels across all of its data. So when you filter for an industry like Software or a title like VP of Sales, you catch every company or person that fits, instead of missing some because another source tagged them differently. That is what makes your lists complete and your targeting reliable. If you are not sure Landbase tracks a particular field, just ask the agent, since it usually covers more than you expect.

The agent layer

On top of the dataset sits an AI agent: you describe what you want in natural language, and it returns a structured list instead of a chat reply.

What the agent does behind the scenes

• It reads your request and works out what you actually mean.
• It selects the right tools for the job, such as search, title expansion, lookalikes, or qualification.
• It runs those tools in sequence and hands back the finished list.

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.

Why it beats a filter

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.

The limits of filters

• Filters only capture the attributes someone thought to add as a field.
• A concept such as companies modernizing legacy systems has no checkbox to tick.
• A multi-step question, such as finding lookalikes of your best customers and then qualifying them, needs several tools run in sequence.

What the agent adds

• The agent understands meaning rather than only keywords, so it can match concepts.
• Because it chains tools together, one request can search, expand, and qualify in a single pass.
• It shows its work, so you can trust the result and refine it.

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.