The DanaOS platform
Dana — Domain-Aware Neurosymbolic Architecture
Physical AI — AI that governs real-world operations — needs a different architecture than digital AI. DanaOS is the layer above models and below workflows, where expertise lives, executes, and compounds.
The newly-possible
DanaOS unifies them into one runtime — combining three capabilities never before combined in a single architecture.
Handle natural language and ambiguity, and generalize across novel situations — the strength of foundation models, without letting them hallucinate on precision-critical tasks.
Reasoning that enforces domain correctness and governance constraints — the strength of ontologies and rules engines, without their brittleness.
Human domain knowledge encoded as structured, actionable intelligence that agents reason from and act upon — not a document to retrieve.
DanaOS enforces strategic determinism where consequences are irreversible, and grants tactical autonomy where conditions allow — running locally, air-gapped if necessary, governed at every layer.
The architecture
A Honeywell or Tokyo Electron engineer can encode expertise for their own domain — without ever touching the runtime. Behavior evolves by editing curated knowledge, not by shipping code.
Runs the See–Think–Act–Reflect loop, dispatches typed steps, and enforces authority and determinism at every step. Hardened once, shared across every deployment.
Declarative and procedural domain knowledge — typed, versioned, and lineage-tracked. Every agent owns a private substrate; there is no shared brain.
Not a subsystem — its own agent, peer to the ones it governs. It reviews evidence and decides, by verdict, what is allowed to enter curated knowledge.
Runs the mission loop, dispatches typed steps, enforces authority and determinism.
Its own agent — running its own STAR loop to decide, by verdict, what may enter curated knowledge. Recording ≠ learning — every change is gated and auditable, with lineage.
A single typed, versioned, lineage-tracked API that fronts everything below — structural & cognitive ontology, your databases & OT, models, and simulators. Curated knowledge is read live; runtime records are appended, never overwritten mid-mission.
Schema · types · relations
Procedural & experiential knowledge
Your systems of record — not a Dana component
Owned specialized & foundation models — SemiKong to your own weights
Digital twins as first-class agents
How agents operate
Every DanaOS agent runs a See–Think–Act–Reflect cycle. Reflect records every outcome with honest verification — the signal a second, governing loop reviews before anything is learned.
Perceive state from sensors, control systems, and operational data.
Reason over the domain ontology using neurosymbolic inference — neural where judgment is needed, symbolic where correctness is required.
Execute a confidence-scored recommendation or a governed action, with human-in-the-loop validation where stakes demand it.
Evaluate the outcome and feed the result back into learning — so the next decision is better.
The knowledge lifecycle
Where retrieval-augmented generation stops at retrieve-and-generate, DanaOS runs the full lifecycle. The decisive additions are Reason, Act, and Learn.
Curate — evidence-backed promotion into typed, lineage-tracked knowledge, not embeddings in a vector store. Reason — neurosymbolic, domain-correct inference rather than next-token prediction. Act — closed-loop execution, not just answer generation. Learn — governed promotion of what proved reliable. This is why DanaOS is an operating system rather than a retrieval tool: it operates within a domain and gets better at operating.
Self-improving
Each scope has an in-loop face and a separative, verdict-gated one. Recording happens every mission; promoting a lesson into curated knowledge is always governed.
New knowledge from authoritative sources — SME procedures, sensor data, tool outputs.
Lessons from specific mission trajectories — what happened, and why.
Cross-episode synthesis: reconciling, generalizing, resolving contradictions.
Compaction of frequently-used plans into compiled, reusable methods.
The compounding moat
Because DanaOS learns at both the ontology and the model layers, every deployment deepens a governed, executable knowledge base that improves with scale — a knowledge network effect a competitor starting today cannot replicate, because it takes the deployments themselves to generate it.
Stickiness comes not from contractual lock-in but from the compounded ontology: a customer can leave, and won't — because leaving means abandoning years of accumulated, executable expertise.
Your data, your ontologies, your deployment, your destiny. No dependency on a foreign cloud; no data leaving your jurisdiction.
Runtime, governance, observability, the managed ontology lifecycle, and continuous capability improvement — the platform never stops getting more capable.
You control your data, ontologies, and deployment — the complete promise, with no transfer of the platform's IP.
The learning frontier
DanaOS's self-improving property has a research trajectory toward predictive world models — the leading edge of making autonomy reliable where a wrong action is irreversible.
Predictive models that learn the structure of normal operation and flag drift, degradation, and incipient failure earlier — with less labeled data, and deployable into current operations.
Simulate the consequences of an action before taking it in the real world — the direct realization of strategic determinism: simulate before you commit.
On framing: the world-model work is a research direction, not a shipping feature — the near-term, deployable thread is earlier anomaly detection, already in motion.
Bring a use case. We'll show you the operating system running your expertise — domain-native, sovereign, and getting smarter with every operation.
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