Ontology, in plain terms
The core ideas behind AssuredFlow, answered simply.
What is an ontology?
A precise, shared definition of the things in your business and how they relate: the entities (like a policy or a claim), the people who act on them, the rules that govern them, and their lifecycles. You write it once, in plain domain language, so your people, your software, and your AI all work from the same meaning.
How is an ontology different from a data model or database schema?
A schema describes how data is stored: tables, columns, and types. An ontology describes what things mean and how they relate, independent of storage. A schema knows a policy row has a status column; the ontology knows what a policy is, who can change its status, which states are valid, and what rules apply. AssuredFlow generates the schema, APIs, UIs, and agent rules from the ontology, so meaning drives the system instead of the other way around.
Why does this matter for AI?
AI agents fail when they have to guess your business rules. When the ontology is the source of truth, every agent action is checked against it, typed, permissioned, and logged. Agents run on your actual rules instead of approximating them.
Is AssuredFlow only for insurance?
No. The platform is domain-agnostic: if you can describe a domain as an ontology, AssuredFlow can build the system for it. We start with insurance and reinsurance because schema drift and audit burden are most acute there.
What do I build, and what's generated?
You maintain the ontology: your entities, rules, and lifecycles. AssuredFlow generates and keeps everything above it in sync, including the database, APIs, UIs, and the guardrails your people and agents operate under.
How does it stay auditable and compliant?
Every change and action traces back to the ontology and an immutable, replayable log. Governance is enforced at the foundation, so audits become a query you can run, and controls map to programs like SOC 2, GDPR, and HIPAA.