Augury monitors rotating equipment with sensors that pick up vibration, temperature and magnetic signals, then uses AI diagnostics to name the fault behind a change. Food, chemicals, paper and consumer goods plants use it to see problems such as bearing wear, misalignment or looseness weeks before a breakdown. Augury does the diagnosis well. The hard part is everything after: getting each finding in front of the right planner, into the CMMS and onto next week's schedule.
Agentic CMMS gives Claude, ChatGPT and other AI agents access to your Augury machine health data. A reliability engineer reviews the whole plant in a few questions, and a planner turns the findings into work without retyping them.
What your AI agent can do with Augury
- Get the plant at a glance. "Which machines have a health status that needs attention right now?"
- Read a diagnosis. "What fault did Augury find on Agitator AG-12, and how severe is it?"
- Follow the recommendation. "What does Augury recommend for the cooling tower fan, and how soon should we act?"
- Track a repair. "Has the condition of the boiler feed pump improved since we replaced the coupling?"
- Rank the risk. "List the five machines with the most serious open findings, worst first."
- Prepare a reliability meeting. "Summarise the new findings across all lines this week, grouped by fault type."
- Find quiet sensors. "Are any monitored machines missing recent data?"
- Plan around a shutdown. "Which findings could we fix during next month's planned stop on Line 3?" The agent lists the machines on that line with open findings and their recommended actions.
Augury with your other systems
- Turn a new diagnosis into a corrective work order in MaintainX, with the fault, severity and recommendation in the description.
- Close the loop: list last month's Augury findings that have no matching work order in Fiix, so nothing slips through.
- Read the maintenance history of a machine in SAP Plant Maintenance (PM) before the planner accepts a new finding, to see whether the same fault came back.
- Post this week's high-severity findings to the reliability team in Microsoft Teams, each with its machine and recommended action.
From diagnosis to done
A diagnosis saves a machine only when someone acts on it in time. The agent makes that step short: read the finding, check the history, check the parts, create the work. Your reliability team keeps its focus on the analysis, and the paperwork stops being the bottleneck.
Creating work orders needs write access, which each person grants to their own agent. Owners and admins choose which roles may use each tool, and every step from reading a finding to raising the work is written to the activity log.