Capability Map
The fastest way to understand what OpenFoundry is trying to deliver is to read its smoke suites as an executable platform map.
Capability Phases Encoded In Smoke
| Phase | Scenario | Main Capability Areas |
|---|---|---|
| P2 | smoke/scenarios/p2-runtime-critical-path.json | connectors, datasets, sync, pipelines, queries, streaming, reports, geospatial |
| P3 | smoke/scenarios/p3-semantic-governance-critical-path.json | ontology, interfaces, properties, governance-oriented workflows |
| P4 | smoke/scenarios/p4-developer-platform-critical-path.json | code repositories, branching, commits, search, developer platform flows |
| P5 | smoke/scenarios/p5-ai-ml-critical-path.json | AI providers, knowledge bases, embeddings, training jobs, model workflows |
| P6 | smoke/scenarios/p6-analytics-enterprise-critical-path.json | analytics datasets, enterprise-tier behaviors, geospatial exploration |
How The Repo Reflects Those Phases
Runtime and data operations
The P2 flow shows the core operational backbone:
- connect to a source
- sync into datasets
- operate on the data
- expose results through pipelines, queries, streaming, reports, and maps
This is reflected in service folders such as data-connector, dataset-service, pipeline-service, query-service, streaming-service, report-service, and geospatial-service.
Semantic and governance layer
The P3 flow shows that OpenFoundry is not only a data movement stack. It also models meaning, interfaces, and governed domain structures through ontology-centric APIs.
That capability is reflected in ontology-service, audit-service, auth-service, and related shared middleware.
Developer platform
The P4 flow demonstrates that the platform also includes repository-like development primitives such as branches, commits, search, and review-oriented flows.
That capability maps cleanly onto code-repo-service, and connects naturally with app-builder-service and marketplace-service.
AI and ML
The P5 flow shows provider-backed AI and ML capabilities as first-class parts of the platform rather than bolt-on experiments:
- provider registration
- knowledge base creation
- document ingestion
- semantic search
- model training jobs
This is represented by ai-service, ml-service, and supporting shared crates such as vector-store.
Enterprise analytics
The P6 flow extends the runtime path into richer analytics and geospatial use cases, reinforcing that the platform is meant to support decision workflows, not only CRUD APIs.
Practical Reading Tip
If you need to understand a product area quickly, start with the matching smoke scenario and then read:
- the corresponding frontend route in
apps/web/src/routes - the service crate under
services/ - the domain contracts under
proto/
That path usually gives you the shortest route from user behavior to implementation.