Data Semantics

Self-explanatory data for AI and people alike

Self-explanatory data for AI and people alike

Teams and AI tools get side-tracked (or hallucinate) when field meanings or segment logic isn’t obvious - it doesn’t matter how clean and structured the data is, if no-one has context on what it represents. Planhat is built for clarity and context-sharing, letting you add plain-language descriptions to fields and save filters with readable names and conditions. This context travels everywhere: dashboards, automations, pages, alerts, and AI prompts alike.

01

Add rich descriptions to any field

Write detailed explanations and examples directly on fields (e.g. what belongs in “Churn Risk Comment” and which keywords matter) so people and agents understand intent at a glance.

02

Save filters that read like natural language

Name and reuse filters with clear titles and visible logic – “High-Value EU Renewals <90 Days” (ARR > $100k AND Region = EU AND Renewal Date ≤ 90d) – across reports, workflows, and AI queries.

Discover the full power of Data Semantics

Discover the full power of Data Semantics

Discover the full power of Data Semantics

FAQ

FAQ

FAQ

What is the role of Data Semantics in how Planhat interprets customer signals?

How do Data Semantics make automation smarter than simple rule-based triggers?

How do Data Semantics help account managers act faster on customer data?

How does a semantic layer handle inconsistent data from multiple source systems?

How do Data Semantics improve the accuracy of AI-driven predictions?