About Luvian
Enterprise AI is failing for a reason that has nothing to do with the model. The data is fragmented, the semantics are missing, and the memory walks out the door every time an engineer leaves. We're building the layer underneath the AI. The engineering context itself.
Our Mission
Every company building autonomous systems already owns the data its AI needs. Requirements, designs, code, tests, telemetry, decisions. The problem is that none of those artifacts speak the same language, and none of them remember.
Luvian is the infrastructure that fixes that. We connect, normalize, version, and govern engineering artifacts into one continuously evolving semantic knowledge graph. The result is an operational memory for your organization that grows with every release, every decision, and every test result.
We believe the moat in enterprise AI isn't the model. It's the context. And we're building the layer that makes that context true.
What We Build
Every capability we ship ladders into one of these.
Decisions, rationale, and trade-offs become machine-readable and continuously evolving. People leave. The memory stays.
A typed, versioned, permission-aware graph of every artifact, link, and decision across your product lifecycle. Beyond RAG, beyond documents.
Anomaly prediction, risk scoring, requirement quality, coverage gaps, compliance readiness. All trained on your operational graph, not generic data.
Who We Serve
Industrial robotics, mobile robotics, cobots, drones, warehouse automation, and the wider mechatronics stack. Anywhere firmware, mechanical, perception, control, and safety have to integrate cleanly. And anywhere AI without trustworthy context is dangerous.
Industrial Robotics
Mobile Robotics
Cobots
Drones
Warehouse Automation
Mechatronics
How We Build
Documents are views of the graph. Not the other way around. Every diagram, requirement, test result, and decision reads from a single semantic core. Change one place, the rest stays consistent.
SysML v2 round-trips. ReqIF, StrictDoc, Cameo sync. Connect your own GitHub for storage and history. Export anything, anytime. No lock-in by design.
Every AI suggestion shows its rationale, its provenance, and its inverse. Confidence is calibrated, not hidden. Acceptance is always the engineer's decision. Always reversible.
One codebase serves commercial cloud, on-prem, and offline-only environments. Bring your own LLM when sensitivity demands it. Safety-critical robotics deployments don't compromise on either rigor or sovereignty.
Why Now
Generic enterprise AI has plateaued. Every CTO building autonomous systems has now seen at least one failed pilot, and the diagnosis is converging on the same place: the model didn't fail, the context did.
The MBSE incumbents are entrenched but architecturally incapable of becoming the graph layer. They were built to be sources, not substrates. The LLM vendors are not going to build engineering ontology or domain reasoning. The compute layer and the context layer are different categories.
That leaves an open category. The next decade of autonomy depends on whether engineering organizations can trust their AI to reason across firmware, mechanical, perception, control, and safety in a single answer. We're building exactly that.
Early adopters help shape the connectors, ontology, and reasoning agents we build first. Limited access through 2026.