Torus

Engineers and project managers on capital projects like substations, pipelines, and treatment plants work with thousands of documents (specs, drawings, datasheets) spread across dozens of firms. Values that must agree across documents, such as design pressure versus relief valve setpoint, often don't get checked until late, leading to costly rework orders (the page cites a $500K example). The page states there are 137,000 engineering firms each on different systems, 3 jobs for every qualified engineer, and 25% of the workforce retiring within 5 years, suggesting teams are stretched thin relative to growing project scale. Existing AI tools are described as chatbots that answer one question about one document at a time, which does not match how engineering review actually works.

Torus is an AI agent platform that ingests project documents (process datasheets, P&IDs, piping specs, load lists, line schedules, etc.), extracts parameters, and cross-references them against each other using named consistency checks (such as relief_valve_SP = design_pressure) spanning more than fifty categories. It flags mismatches as critical or warning items, shows coverage across document types, and surfaces an open review queue for engineers to confirm or dismiss. Beyond flagging, Torus drafts follow-up actions in the firm's own format, such as RFIs to process engineering or procurement approval memos, and tracks threads across multiple parties to flag stalled approvals or missed vendor deadlines, with full traceability on every output. Customers can run Torus in a managed cloud mode with isolated tenancy or in a sovereign mode where the model runs entirely inside their own VPC or servers, and the company states SOC 2 Type 1 and 2 certification is in progress.

The page argues capital projects are growing larger while engineering teams are not, citing 137,000 fragmented firms, 3 jobs per qualified engineer, and 25% of the workforce retiring within 5 years.