Semantic Views for Property Data
Context. Confidence. Answers.
Give AI the Context Behind Your Property Data
Access to high-quality property data is only part of building effective AI and analytics solutions. AI also needs to understand what the data means, how it connects and how it should be queried.
Semantic Views provide that context.
Built on top of ATTOM’s property data delivered through Snowflake, Semantic Views define the relationships, business terminology, metrics, synonyms and query logic needed to help AI systems work with the data correctly. ATTOM has already built that context, so organizations don’t have to.
The Semantic Context Is Already Built
Building your own semantic layer means understanding ATTOM’s schemas, relationships, terminology, metrics and query logic before your AI systems can reliably work with the data.
ATTOM has already done that work.
Semantic Views deliver ATTOM’s understanding of its property data alongside the data itself, helping organizations reduce data modeling work, accelerate AI adoption and spend more time putting property data to work.
Semantic Views built around ATTOM’s property data aren't static models. They are actively evaluated and refined as new questions, use cases and data needs emerge. Insights from real-world usage help ATTOM strengthen definitions, relationships, metrics, verified queries and other semantic context over time.
From Property Data to AI-Ready Context
Semantic Views are built using Snowflake’s native capabilities and sit directly on top of ATTOM’s property data.
1
Start With ATTOM
License ATTOM’s property data products your organization needs.
2
Add Semantic Views
Dedicated semantic models provide context for supported ATTOM data products, including field descriptions, synonyms, relationships, governed metrics, join logic, sample values and verified queries.
3
Connect to AI and Analytics
Use the semantic layer with Snowflake Cortex Analyst, Cortex Agents, and other supported experiences to help translate business questions into governed queries.
4
Ask Questions in Natural Language
Users can ask questions about ATTOM data without needing to know the underlying table names, joins or SQL syntax.
Ask Your Property Data Questions in Plain English
With Semantic Views, ATTOM’s property data can become accessible through conversational analytics.
A user might ask:
How many properties sold in Montana in the last week?
Instead of requiring the user to identify the correct tables, fields, joins and calculations, the semantic layer helps Cortex Analyst translate the question into SQL using ATTOM-defined relationships and metrics.
Users receive a natural-language answer and can inspect the underlying SQL, providing greater transparency into how the result was generated.
What are Semantic Views?
Semantic Views provide a semantic layer built on top of ATTOM’s property data delivered through Snowflake. They provide AI and analytics tools with context about how ATTOM’s data should be understood and queried, including relationships, business definitions, metrics, synonyms and query logic.
Unlike a static semantic model, Semantic Views are actively evaluated and refined by ATTOM as usage and data needs evolve.
Make Your Property Data AI-Ready
Unlock the value of ATTOM with Semantic Views in Snowflake.