Every business has a model. It lives in the heads of your best people — how customers relate to products, how teams relate to goals, how campaigns drive revenue, what KPIs matter and what they mean. When those people leave, that understanding leaves too. Guardian makes it permanent, structured, and queryable.
What You Define in the Ontology
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Objects
Customers, products, teams, campaigns, accounts, projects, assets — every entity your business cares about becomes an object in the ontology with properties, states, and metadata.
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Relationships
How objects connect to each other. A customer has accounts, accounts have products, products have KPIs, teams own campaigns. These relationships are what give AI context — not isolated data points, but connected meaning.
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KPIs & Metrics
Define what matters: revenue, churn rate, NPS, support response time, pipeline velocity. Each KPI is connected to the objects and relationships that drive it, so AI understands not just the number but what's moving it.
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Rules & Thresholds
Business rules that govern operations — if churn exceeds 5%, escalate. If pipeline drops below target, alert the VP of Sales. If NPS trends down for three consecutive months, trigger a review. The ontology encodes your operational logic.
Why This Is Different from a Data Model
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A data model tells you what tables exist. An ontology tells you what your business means.
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A data model stores rows. An ontology connects understanding.
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A data model requires queries. An ontology enables reasoning.
Traditional approaches force AI to figure out your business from raw data — table structures, column names, JOIN paths. Guardian gives AI a pre-built understanding: this is a customer, this is their health score, this is why it's dropping, these are the levers to fix it.