Hover any data type for a summary, then click for the full picture — use cases, real-world examples and what the data looks like.
Why the join key matters for AI
Every signal and every record is mapped to one HG company entity — across subsidiaries, corporate hierarchies and buying centers worldwide. That mapping took years to build and is maintained continuously. It is what lets an agent see a coherent account instead of a pile of disconnected signals.
Technographics, intent, spend and contacts for one account come through as a single resolved company, not four sources to match on name and domain.
Entity resolution is the step where most pipelines break. It is done before the data reaches you, so the model is not reasoning over duplicates.
When the grounding context is complete and consistent for an account, the model has less reason to fill gaps with invention.
All data types, by group
Search across every data type’s description, use cases and questions.
Who the company is, and how it's connected
What they run, and where they run it
What they invest, and how far along they are
IT Spend
Modeled technology budgets at the company and category level
06AI Spend
Standalone AI investment modeling across hardware, software, and services
07Cloud Maturity
Assessment of a company's cloud adoption journey and posture
08AI Maturity
Assessment of a company's artificial intelligence adoption sophistication
What they're researching, saying and buying now
Who to reach, and what they've already signed