Built for an autonomous future
Your requirements, designs, code, tests, telemetry, and decisions already exist. They just don't talk to each other. Luvian connects them into one continuously evolving knowledge graph, so AI reasoning, traceability, and engineering automation can finally be trusted.
Your AI already has access to the data it needs. The problem is the data itself: disconnected, conflicting, stale, permission-fragmented, and semantically weak. That isn't a model problem. It's a context problem.
Requirements live in one tool, decisions in chat, code in another, tests in a third, telemetry in a data lake. None of it speaks the same language.
LLMs see strings of text. They don't see joints, sensors, control loops, interfaces, or safety constraints. Without typed engineering meaning, reasoning hallucinates.
When an engineer leaves, the rationale walks out with them. The same decisions get re-litigated. Tribal knowledge quietly evaporates from one release to the next.
Luvian fixes the layer underneath the AI. The engineering context itself.
"The moat in enterprise AI isn't the model. It's the context."
You already own the data your AI needs. Luvian is the infrastructure that normalizes it, connects it, versions it, governs it, and gives it engineering meaning. The result is an operational knowledge graph that grows with your organization. Your AI finally has something true to reason over.
Each layer builds on the one below it. Together they form a context layer your AI can actually use.
Connectors into every system where your engineering knowledge already lives.
Issue trackers · Requirements platforms · System modelers · Source control · CI/CD · PLM · Test management · Simulation outputs · Telemetry · Standards & documents · Unstructured comms
A canonical engineering ontology with stable identity, lineage, ownership, and temporal state.
Requirement · Capability · Function · Interface · Signal · Component · Joint / Actuator · Sensor · Control Loop · Safety Constraint · Hazard · Failure Mode · Scenario · Test Case · Variant · Release
Beyond RAG. Permission-aware retrieval, graph traversal, temporal reasoning, contradiction detection, and confidence scoring.
Relevance ranking · Traceability inference · Provenance · Domain-aware embeddings · Conflict surfacing
Copilots, reviewers, and domain agents that earn trust because the substrate underneath them does.
Impact analysis · Safety review · Architecture suggestion · Test synthesis · Compliance scoring · Regression investigation
One question that would normally cost three years of tribal knowledge. Answered by reasoning across the graph.
A user asks
"Why did our pick-and-place positional accuracy regress after Firmware 4.7?"
Luvian autonomously connects
That isn't retrieval. That's contextual systems reasoning over a live engineering graph, across firmware, mechanical, perception, and safety domains in a single answer.
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.
Anomaly prediction, risk scoring, requirement quality, coverage gaps, compliance readiness. All trained on your operational graph, not generic data.
We started with the structured-artifact layer: a SysML v2-native modeler. It's the seed of the broader knowledge graph. A typed, collaborative, AI-assisted source of truth for system architecture, and the foundation the rest of the platform federates around.
Drag-and-drop blocks, ports, interfaces, and connections with full SysML v2 compliance. Every element feeds the graph as a typed semantic entity.
Multi-user editing on the shared semantic model, with presence, awareness, and operation-level undo.
Intelligent suggestions for properties, ports, traceability links, and requirement quality, all grounded in your graph.
SysML v2 JSON, ReqIF, and ontology-aware exports. Your data, your format, no lock-in.
Issue trackers, requirements platforms, system modelers, source control, CI/CD, PLM, test management, simulation, and telemetry, all pulled into one canonical graph.
Canonical types with stable identity, lineage, temporal state, and ownership across every artifact.
Permission-aware retrieval, graph reasoning, contradiction detection, and confidence scoring beyond traditional RAG.
Safety review, requirement quality, impact analysis, test synthesis, and compliance agents grounded in your engineering context.
Provenance, confidence scoring, audit trail, and contradiction surfacing. So AI output is reviewable, not just persuasive.
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
Early adopters help shape the connectors, ontology, and reasoning agents we build first. Limited access through 2026.
We'll reach out when your access is ready.