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 your Cognitive Ontology, expertise made executable, lives, runs, and compounds.
The DanaOS architecture
Vertical packs compose shared foundations — they don't fork them. The runtime never changes per domain.
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 judgment encoded as a Cognitive Ontology — structured, executable knowledge 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
Domain applications and agents evolve independently from the hardened runtime. External systems connect through governed adapters; knowledge changes only through versioned, auditable promotion.
Cross-cutting plane
An Operational Pack is the broad product unit: shared foundations plus domain knowledge, computations, models or simulations, methods, views, policies, actions, and adapters. It composes what it needs; it does not fork a vertical-specific runtime.
Composition, not duplication
Building systems, spaces, zones, HVAC topology, comfort-energy methods, BMS bindings.
Plants, equipment trains, process-flow topology, engineering constraints, DCS/SCADA bindings.
Fabs, tools, recipes, lots, process states, quality, and yield.
Assets, load, reliability, tariffs, markets, and energy-system context.
Sites, mobile equipment, ore flows, production, maintenance, safety, and spatial context.
Wells, facilities, process flows, integrity, production, and field operations.
Vessels, machinery, routes, cargo, maintenance, safety, and fleet operations.
Knowledge · computations · models / simulations · methods · views · policies · actions · adapters
Reconcile identity across BIM, P&IDs, tags, asset registries, and enterprise systems.
Read state from BMS, DCS, SCADA, PLC, historian, and event systems.
Execute engineering calculations, mechanistic models, learned models, and optimization.
Dispatch validated recommendations or actions through identity, constraints, approval, and verification.
Every physical action passes identity and authorization checks, domain constraints, consequence estimation where applicable, risk-based approval, a governed adapter, outcome verification, and evidence capture.
Governed action ladder
DanaOS advises and, under your authority, writes within governed setpoints. It does not bypass your interlocks or touch the safety-instrumented layer. DanaOS integrates existing sensors and systems; it is not a sensor platform.
How agents operate
A DanaOS agent is not one big autonomous loop. STAR is the leaf execution loop inside a governed action lifecycle: intent is bound to a pinned procedure, and every branch, gate, and authority check is enforced by the lifecycle envelope. Bounded See–Think–Act–Reflect loops run only at the leaf steps that genuinely need judgment — each inside its own authority envelope, each verified before its outcome re-enters the graph.
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 the Cognitive Ontology: 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. DanaOS is the operating layer above models and below workflows — not a kernel or a replacement for control systems.
Self-improving
Each scope has an in-loop face and a separate, verdict-gated one. Recording happens every mission; promoting a lesson into the Cognitive Ontology 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 run deepens your Cognitive Ontology — a governed, executable knowledge base inside your walls. It takes the operations themselves to generate it; no generic model, and none of your competitors, can shortcut that.
Not lock-in — an asset that appreciates: years of your best engineers' judgment, typed, versioned, and executable. And it's yours.
Your data, your ontologies, your deployment, your destiny. No dependency on a foreign cloud; no data leaving your jurisdiction.
Built on open-weight foundations you can inspect, fine-tune, and run inside your walls — specialized on your domain, with no closed API in the loop and no data leaving to train someone else's model.
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.
Enterprise-grade
What it takes to put autonomous agents on mission-critical, regulated operations — designed in, not bolted on. Enforcement lives in the runtime, where a prompt can't reach it.
On-premise, at the edge, or fully air-gapped. Your data and models never leave your perimeter.
Every call toward physical equipment passes a deterministic enforcement point — identity, authorization, parameter bounds — that refuses unsafe commands without consulting the model, even with the network down.
An unclassified tool is treated as if it touches equipment — the most restricted class. Absence of classification is never permission, and enforcement is on by default.
Every decision joins a cryptographically chained log written inside your walls — PROV-O lineage a regulator can follow, verifiable even for the weeks you ran disconnected.
Authorization is connection-aware: a broad envelope online, a pre-staged bounded one offline. If the runtime can't tell which, it assumes disconnected — agents never fail open.
Overlays the stack you already run — SCADA, BMS, DCS, historians, and enterprise data — under the same role-by-layer access matrix.
Deploy where the work is
Site-to-site variance lives in a curated Operational Pack, not in rebuilt integration code — so the second site is faster than the first, structurally.
Modular integration to your industrial data sources and control systems.
Prove value on one asset, then expand across lines and sites on the same runtime.
Deployment patterns, security posture, and agent governance you can replicate site to site.
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 layer running your expertise — domain-native, sovereign, and getting smarter with every operation.
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