Sight Machine

Sight Machine's argument is that the bottleneck in manufacturing AI is not the model, it is the data context. Raw industrial signals from controls, historians, MES and ERP do not arrive as tidy events; the Manufacturing Context Layer is the piece that turns them into production events and KPIs an agent can reason on, and your team can audit. If you have already tried pointing a general-purpose AI tool at historian exports, you know why that layer is the product. Above it, Dynamic Production runs agents that investigate continuously and build industry-specific ML tools along the way. The important design choice is that findings are validated before becoming live recommendations — a human process expert stays in the loop during data connection and contextualization. That is honest about how plant change actually gets approved, but it also means the platform will not run itself on day one; you need people who understand the line. Enterprise Agents is where this differentiates from traditional MES and historian vendors that stop at collection. Publishing manufacturing intelligence as an MCP server means an agent in Microsoft Teams, an NVIDIA Omniverse simulation or a Databricks ML model can pull plant-floor AI in and push results back. That two-way flow — production linked to supply chain, logistics and demand — is the firmwide optimization story, and the references back it: Toyota Industries' paint shop live on Microsoft Azure, plus NVentures among the backers. Be clear-eyed about fit. Value depends on having OT and IT systems to connect, so a shop with thin infrastructure gets little. The vendor does not disclose which foundation models power the agents; the evidence describes proprietary industry-specific ML tools and the Context Layer, not named LLMs. Deploying "in days" still assumes your data sources and process experts are ready. And if your AI roadmap genuinely ends at dashboards and historical reporting, this is a different category of purchase — do not buy it for reporting.