General-purpose AI can't make sense of raw industrial data. The Semantic Model turns it into something agents can reason on and your team can trust.
Sight Machine connects directly to all plant systems and more — controls, historians, MES, ERP, and document repositories — no custom middleware. SOPs, P&IDs, and line diagrams come in the same way; agents extract structure and meaning automatically.
Blueprint, our proprietary language model, maps tags to the physical process automatically — looking beyond field names to introspect data, recognize patterns, and infer meaning. This is one of the reasons the Semantic Model deploys in days instead of months.
Sight Machine constructs an operational digital twin — characterizing every asset and material flow, then building up lines and plants by capturing interactions between steps.
Agents shape raw signals into production events — cycles, downtimes, quality events — and the KPIs your team actually runs the plant on. Your experts review the semantic model with inline validation, seeing what each is before publishing.
The agent does the first pass — extracting, mapping, inferring, drafting — and your experts confirm, adding tacit knowledge the agent can't see. Every signal and KPI has provenance: created by an agent, approved by an expert.