Industrial AI · ALiGN
ALiGN is an AI-native industrial enabler. We do not build another system beside yours — we build, on top of PLM, ERP, MES and OA,Industrial AI Operation Layeran industrial AI operation layer that unifies business semantics, orchestrates agents, verifies permissions and state, and preserves process evidence. Your systems of record keep the books; we make AI understand the process, advance the action and leave the evidence.
Between general-purpose AI and industrial scenarios lies a gap nobody in this industry has truly filled.
They understand language and reasoning, but not process know-how, and they cannot connect to shop-floor systems — capability stops at the demo and never enters real workflows.
Years of domain depth, but architectures built for process, not for AI-native — only bolt-on patches, never a re-architected business.
Close the adaptation gap between general-purpose LLMs and industrial scenarios, and build the industrial infrastructure that makes AI land.
AI adoption is not “a few more tools” — it is about making the businessclearer, AI more governed, processes more traceable。
On top of your existing systems we build an industrial AI operation layer — unifying interaction, business governance, technical enablement and semantic fusion, so AI truly enters the process, drives the systems and leaves evidence behind.
Built for manufacturing scenarios, it connects the full chain — XR content editing, AI algorithm enablement, scenario deployment and multi-device adaptation — and turns it into reusable enterprise knowledge assets.
INTELLiU governs how AI is governed and orchestrated inside business processes — a unified interaction entry point, identity and permissions, a runtime engine and process evidence;INTELLiR governs how XR content is produced and delivered to the shop floor — content editing, algorithm enablement, scenario deployment and multi-device adaptation. They solve different problems: each can be delivered and deployed on its own, or adopted in sequence as a company's digital maturity grows.
Connect agents into existing systems and unify identity, permissions, guardrails and audit, so every action can be replayed.
From XR content editing to multi-device delivery in one continuous chain, accumulating reusable enterprise knowledge assets.
Describe it in natural language to generate a factory simulation model, then validate line and scheduling plans before committing.
In 90 days, turn “buying an AI tool” into “building an industrial intelligence asset you can run on and on”.
Map out business objects, processes, states and rules, and translate the scenario into an AI-readable business map.
Run the smallest viable loop — map, persona, engine and evidence chain — on one real scenario.
Move into production, run in parallel with existing systems, and verify stability and business value.
Use Trace to review benefit and risk, then replicate proven scenario packages across more lines and plants.
Less searching, form-filling, shuttling between systems and waiting.
Reduce the risk of omissions, privilege violations and wrong versions.
Accumulate process drafts, empirical rules, traces and reusable scenario packages.
Align AI cost with business workload and scenario ROI.
We only sell what we run ourselves. Start with a 90-day co-creation.
