Ontology

The ecosystem.Ai Ontology is how you discover and share the concepts the platform uses: projects, predictors, offer matrices, invocations, contact logs, and how they connect.

It is the semantic layer over:

Workbench2 — configuration entities in ecosystem_meta
ecosystem-server — modeling projects, diagrams, generation
ecosystem-runtime — real-time /invocations and /response

Like an operational ontology elsewhere, it connects digital assets (Mongo collections, deployments, models) to their counterparts (customers, offers, channels) so products reason consistently about decisioning.

How to use this documentation

Goal	Start here
Learn why ontology exists	Why create an Ontology?
Browse every class & property	Concept Index
Workbench meta concepts	Entities · full catalog
Runtime API & logging	Runtime · full catalog
Modeling server concepts	Server · full catalog
Fixed offer catalog shape	Offer Matrix
Open customer / feature docs	Customer Feature Lookup
Machine-readable downloads	Downloads
Term lookup	Glossary

Modules at a glance

Module	Namespace	Concepts
Entities	`…/ontology/entities#`	Project, Frame, Predictor, DynamicEngagement, OfferMatrix, Campaign, Contacts, DeploymentStep, …
Workbench	`…/ontology/workbench#`	Solution cases, AsyncJob, SyntheticDataRequest, RuntimeConsole
Server	`…/ontology/server#`	ModelingProject, Diagram, OntologyContext, generation jobs
Runtime	`…/ontology/runtime#`	InvocationRequest/Response, ContactLog, ResponseLog, plugins, Option
Platform	`…/ontology/platform#`	Umbrella + alignments

Full IRIs use the base https://ecosystem.ai/ontology/. See Namespaces.

Two schema rules (critical)

Offer Matrix rows are closed — offer_id, offer_name, cost, price, category, static_offers
Customer / feature lookup is open — any document shape keyed by customer (or configured lookup key)

Formal artifacts

Turtle (OWL 2 + RDFS + SKOS) and SHACL shapes are published under /ontology/*.ttl. Use them in tooling, synthetic data grounding, or as the contract for integrations.