Enterprise AI that knows what it means.
Every computing era has had a foundational platform layer. The database made transactions trustworthy. The data warehouse made analytics possible at scale. The semantic execution layer is what makes enterprise AI real — governed, accurate, and auditable by construction.
CloudSense AI’s mission is to build that layer.
Every enterprise generates more data than it can interrogate. Teams build NL2SQL POCs on the same warehouse with different definitions of revenue. Agents bypass governance by rephrasing queries. Models hallucinate because no one encoded what the data means before asking them to reason about it. The answer isn’t a better model. It’s a semantic execution layer — the missing middleware between enterprise knowledge and enterprise AI.
Between us, we’ve built at Oracle, HP Autonomy, Sun, and Intel — enterprise data, knowledge search, and compute infrastructure at scale. We know exactly why AI breaks down in large organizations. CloudSense is how we fix it.
We hope you’ll trust us with your enterprise AI.
Thinking Sense · CloudSense AI
“The ultimate goal is to bring our chaotic observations to our senses, giving them a form and content that finally makes logical sense.”
George Caspar Homans — Coming to My Senses, 1984
Homans wrote about making sense of the world through structure — turning raw, fragmented observation into something that holds together logically. That is exactly what CloudSense does: ingest the chaos of enterprise data and give it a form — a typed, governed semantic graph that grounds every AI query before the model acts.
On the name
The five basic senses — sight, sound, touch, taste, smell — are inputs. What the brain does with them is something else. Humans layer those raw signals into perception, pattern, memory, and judgment: a higher-order faculty that doesn’t have a clean name in biology, but that we might call thinking sense. It is the sense that synthesizes all the others. The one that turns observation into understanding, and understanding into action.
We believe the same evolution is happening in machines. Humanoids and autonomous systems are acquiring basic senses fast — cameras, microphones, LiDAR, vibration, pressure. But raw sensing isn’t intelligence. The gap between a sensor reading and a good decision is exactly the gap between data and meaning. That gap is what we build for.
Thinking Sense, Inc. exists to close it — first in the enterprise, where the “sensors” are databases, warehouses, and APIs, and the decisions are queries, reports, and AI-driven actions; and eventually at the edge, where physical machines need the same semantic grounding to act reliably in the world. The name is a commitment: not just to process signals, but to make them mean something.
Meaning before the model
The failure isn’t the model. It’s what the model doesn’t know.
The Fortune 2000 has already invested in foundation models, built RAG pipelines, hired AI teams — and is now confronting what those investments cannot solve. Models don’t know the business. Five teams have five definitions of revenue. Governance is advisory, which means it arrives too late.
CloudSense enters as the semantic execution layer that makes existing AI investments work — not a replacement for the models, clouds, or systems enterprises have built. One typed semantic graph. One definition per metric. Compile-time policies that enforce themselves. That is a CIO-level conversation in every Fortune 2000 account.
One definition per metric
Revenue, margin, headcount — encoded once in the semantic graph, inherited by every query, every agent, every tool.
Grounded before execution
Every query term is resolved against the semantic graph before SQL is emitted. Models can't invent definitions they weren't given.
98–100% NL2SQL accuracy
Raw NL2SQL benchmarks at 31%. With a semantic layer, 87%. With CloudSense compile-time grounding, 98–100% on covered questions.
Governance
Advisory governance is a fire alarm. You find out after the damage.
Every enterprise AI deployment we’ve seen governs the same way: rules defined in a catalog, applied after execution, surfaced in logs. By then the query has run, the data has crossed a boundary, and the audit has failed.
We believe governance belongs at compile time. When CloudSense assembles the SQL, RBAC and ABAC predicates are injected before the query reaches the warehouse. Unauthorized queries don’t get filtered — they fail to compile. That isn’t a feature you configure. It’s structural.
Compliance isn’t a feature you turn on. It’s an architecture you build from.
Compile-time enforcement
RBAC + ABAC predicates injected during SQL generation — before the query reaches the warehouse, not after.
Queries that can't bypass rules
AI agents can't rephrase their way around governance. Unauthorized queries fail to compile — the path to the warehouse is closed.
Autonomous drift detection
Metric definitions and entity relationships change. CloudSense detects semantic drift automatically — no manual curation required.
Sustainability
The most sustainable datacenter is the one you don’t build.
A cloud inference call consumes roughly 10× the energy of the same inference on a modern NPU. Multiply that by the trillion enterprise queries this decade will generate, and the environmental cost of defaulting to the cloud becomes hard to justify — especially when the alternative is already in the pockets and on the shop floors of your workforce.
EdgeSense routes inference to the device first, escalating to CloudSense only when confidence demands it. Most queries resolve locally. The ones that don’t are escalated selectively, not reflexively. Dramatically lower energy and water consumption per query — without sacrificing accuracy or governance.
7 billion smartphones are active globally. Every new flagship ships with a dedicated NPU. We should use them.
more energy per cloud inference vs. on-device NPU — by architecture, not by policy
smartphones in active use — every new flagship ships with a dedicated NPU already on premises
means most queries never reach a datacenter — energy and water savings are structural, not optional
Physical world
The physical world is being digitized. It will need a semantic layer.
Travis Kalanick’s ATOMS and Andreessen Horowitz’s $1.7B investment signal what the next decade looks like: factories, utilities, logistics networks, and facilities rewired with sensors, connectivity, and compute. Every asset becomes observable. Every system becomes programmable.
That creates an enormous unsolved problem. A digitized facility generates continuous streams of operational data — but the semantic layer that turns those streams into governed, auditable decisions does not yet exist at scale. Cloud AI can see the data. It still cannot know what it means in this building, for this asset, under these procedures, right now.
CloudSense is built for exactly this. EdgeSense delivers a scoped, signed slice of the semantic graph to the device at the moment of action — offline, sub-second, and CIP-compliant. Data that must stay local never leaves. Decisions that require escalation find their way back. The semantic layer travels with the work.
Infrastructure as a semantic network
Every digitized asset generates data. CloudSense resolves that data into governed meaning — turning raw telemetry into decisions the business can audit and act on.
Intelligence at the point of action
Physical decisions happen on-site, often offline. EdgeSense delivers a governed semantic slice to the device — maintenance, inspection, dispatch, response — without a cloud round-trip.
Sovereignty by architecture
CIP-classified telemetry never leaves the device. Only non-classified content escalates to CloudSense. Compliance isn't a policy — it's enforced at the edge by construction.
The world is not short of sensors. It is short of AI that knows what they mean.
Enterprise AI doesn’t need more models. It needs to know what the data means before it acts.