Pipelines as state machines
Work is modeled as pipelines with explicit states — not as one-off scripts that either finish or vanish. If something fails midway, the runtime knows exactly where it stopped.
ORION Core
The operational runtime behind ORION.
ORION Core is not an application you open. It is the runtime that keeps pipelines running, tracks what happened, and turns scattered information into governable, recoverable capability.
Retrieval — turning documents into context for local AI — is one of the pipelines this runtime executes. The runtime itself is the foundation.
Foundation
A persistent runtime, not a request/response app.
Recovery and replay
Nothing critical lives only in memory. Pipelines can be resumed, retried, or replayed from a recorded history — failures become recoverable steps, not lost work.
Jobs, queues, workers
Long-running and background work is handled by persistent jobs and workers, decoupled from any single request — so operations continue even when nobody is watching.
Event model
The system knows what happened, and when.
Domain, command, and system events
Every meaningful change — a document ingested, a pipeline advancing, a job completing — is recorded as an event. This is the operational memory the system runs on, not just a log for humans to read later.
Event-driven by default
Components react to events instead of being tightly wired together. New capabilities can subscribe to what already happens in the system without rewiring existing pipelines.
Traceable by construction
Because state changes are events, the path from a document landing in the system to an answer being generated can be reconstructed — not just inferred.
Pipelines
Retrieval is a pipeline the runtime runs — not the whole system.
DocumentControlled intake of files and institutional content.
ProcessingExtraction, cleaning, normalization, and metadata enrichment.
ChunkingSegmentation into semantic units useful for retrieval.
EmbeddingsLocal vector representation for meaning-based search.
Hybrid retrievalCombination of semantic and lexical search.
RerankingReordering the most relevant passages before the answer is generated.
ContextAssembly of evidence to reduce vague or disconnected answers.
LLMDecoupled final generation, with support for different local backends.
This is one pipeline among others the runtime can host — each one a sequence of states, events, and recoverable steps.
We are not trying to build just another AI interface. We are building the runtime underneath it.
Observability & isolation
Visibility and boundaries as part of the architecture.
Observability as a contract
Every subsystem is expected to expose what it is doing — not as an afterthought added for debugging, but as part of what it means to be a component in this runtime.
Multi-tenancy by design
Isolation between teams, projects, or organizations is treated as a foundational property, not something retrofitted later at the cost of rework.
Headless-first
The runtime operates independently of any interface. APIs and UIs are entry points at the edge — not where the system's core logic lives.
Local-first
Designed to operate close to the data, reducing exposure and external dependence.
Modular
Decoupled components enable gradual evolution across pipelines, retrieval, reranking, and inference.
Auditable
Traceability, metadata, and observability as part of the architecture, not as an afterthought.
Engineering principle
The complexity of modern artificial intelligence is inevitable. The operational complexity imposed on users should not be.
ORION is designed around a simple premise: advanced AI infrastructure should increase autonomy, not operational friction.
Foundational architecture
Engineering Principles
ORION is not built solely around model performance, but around durability, auditability, infrastructure sovereignty, and long-term operational clarity.
Every architectural decision follows explicit principles designed to reduce dependency, preserve transparency, and keep AI systems controllable as they scale.
Explore Principles