How the meaning your business owns reaches your platform.
Two things have to happen for AI to answer with your definitions: the meaning has to be deployed into Snowflake or Databricks, and the data layers behind it have to be built and checked. This page shows how both work — in terms a business owner can follow.
Your definitions, deployed where your AI reads them.
The Native Apps turn the definitions your business owns into the semantic objects Snowflake and Databricks AI actually read — and keep them in step as things change, with review before anything ships.
From business meaning to platform execution
Defined by the business
Net revenue
= gross revenue − discounts − credits
- Owner
- Finance
- Used for
- Quarterly reporting
Model, mappings and native output
Your business meaning, ready for AI
One agreed definition with an owner, a time grain and the words people use to ask for it.
Choose your platform
Your platform executes the calculation. Pick one; you don't need both.
Answer in Cortex Analyst
Net revenue grew 5.4% from Q2 to Q3.
Definition used: net revenue = gross revenue − discounts − credits (owner: Finance), served through Semantic Views.
Fictional example
When a source column changes
A rename in a source system surfaces the one affected mapping for review. The definition your business agreed does not change — only the connection is updated, explicitly.
Mapping and native output match the observed schema.
1 · Observed schema
DEMO.GOLD.REVENUE_BY_QUARTER
Snowflake database DEMO, schema GOLD, table REVENUE_BY_QUARTER
- CREDITS NUMBER(18,2)
- DISCOUNTS NUMBER(18,2)
- GROSS_REVENUE NUMBER(18,2)
- QUARTER DATE
2 · Mapping
| Logical | Physical |
|---|---|
| gross_revenue | GROSS_REVENUE |
| discounts | DISCOUNTS |
| credits | CREDITS |
Business definition unchanged: net revenue = gross revenue − discounts − credits.
3 · Totals
- Q2 net revenue
- $9.3m
- Q3 net revenue
- $9.8m
Mapping current
format: schema.yaml
version: "1.0"
schemas:
- source:
database: DEMO
schema: GOLD
type: snowflake
tables:
REVENUE_BY_QUARTER:
columns:
CREDITS: {type: "NUMBER(18,2)", is: [notnull]}
DISCOUNTS: {type: "NUMBER(18,2)", is: [notnull]}
GROSS_REVENUE: {type: "NUMBER(18,2)", is: [notnull]}
QUARTER: {type: DATE, is: [notnull]}name: revenue_semantics
description: Fictional quarterly revenue example.
tables:
- name: revenue
base_table:
database: DEMO
schema: GOLD
table: REVENUE_BY_QUARTER
time_dimensions:
- name: quarter
expr: QUARTER
data_type: DATE
facts:
- name: gross_revenue
expr: GROSS_REVENUE
data_type: NUMBER
- name: discounts
expr: DISCOUNTS
data_type: NUMBER
- name: credits
expr: CREDITS
data_type: NUMBER
metrics:
- name: net_revenue
expr: SUM(gross_revenue) - SUM(discounts) - SUM(credits)Fictional example. Nothing here runs against a warehouse.
The data layers behind the answer, built with your team.
Agreed definitions still need data shaped to match them. Our engineers work alongside yours to build the transformations, facts and dimensions behind each measure — in your repositories, to your standards — and prove the output matches the source.
- 01
With your engineers
Delivered alongside your team and their AI coding and deployment tools.
- 02
Your Git, your standards
Code lands in your repositories, reviewed under your conventions.
- 03
Your platform runtime
Runs in Databricks or Snowflake through your deployment workflow; existing ingestion stays as it is.
- 04
Facts and dimensions
Silver-to-gold transformations shaped by business definitions.
- 05
Maintained formulas
Definition and schema changes handled as reviewed updates.
- 06
Reconciliation
Source → silver → gold → semantic checks on each change.
Start a free pilot
Pick one workflow: a semantic model to deploy, or the facts and dimensions behind a business question. We agree the scope of a free pilot together before we start.
- 01One business question
- 02One owner of the definition
- 03The data it depends on
- 04A review on your platform
- Start a free pilotWatch demo
