We are building an AI-assisted workspace that helps reliability teams organize source evidence, draft maintenance analysis, review equipment observations, and prepare work packages while keeping engineering judgment explicit.
Reliability strategy, condition monitoring, and maintenance planning frequently live in separate tools. That separation can strip source context from a decision, create duplicate data entry, and make ownership difficult to follow as work moves between teams.
AndonEAM is designed to connect three workflow areas: Strategy, Monitoring, and Planning. Where the required data and integrations are available, monitoring evidence can inform draft recommendations and reviewed outputs can be prepared for an approved downstream handoff.
We design around documented engineering tasks, with source context and review states visible to the people responsible for the decision. Generated analysis is a draft to evaluate, not a substitute for professional judgment.
AI can draft recommendations; designated reviewers approve, revise, or reject them before downstream use. Approval gates and responsibilities are agreed during implementation.
Our stated practices include encrypted transport and storage, tenant-level access controls, and no use of customer operational data to train shared models without authorization. The applicable controls and contractual scope are reviewed during onboarding.
Where configured, reviewed recommendations can be converted into structured work packages or exported to an existing maintenance system. People and source systems retain control over approval, scheduling, and execution.
Bring together manuals, P&IDs, maintenance history, and datasheets. AndonEAM can draft FMEA elements, consequence categories, and maintenance-task candidates for structured engineering review. Source quality and asset scope determine the usable output.
When suitable operational data is connected and validated, AndonEAM can organize health indicators, flag observations for review, and preserve the evidence behind a monitoring assessment.
Reviewed strategies can be prepared as structured maintenance work packages and, where a supported integration is configured, exported to an existing EAM or CMMS. Connector scope is confirmed during implementation.
Reliability teams often work across separate strategy files, condition-monitoring tools, maintenance systems, and document libraries. That fragmentation makes it difficult to preserve decision context as work moves from analysis to review and execution.
Rather than replace the CMMS, we chose to explore a connected decision-support layer: one place to assemble evidence, draft analysis, coordinate review, and prepare approved outputs for downstream systems.
We are building and validating workflows with an emphasis on understandable source context, explicit approval states, and implementation boundaries. Capabilities are evaluated against the data and governance needs of each deployment.
Our direction is a governed reliability workspace that can support teams from strategy development through condition review and maintenance planning, while integrating with the systems that remain responsible for record and execution.
Extraction equipment, conveyors, crushing, and material handling systems.
Refineries, pipelines, offshore platforms, and petrochemical processing.
Power generation, substations, water treatment, and distribution networks.
Continuous processing, reactors, separation units, and batch production.
Production lines, CNC systems, packaging, and complex discrete manufacturing.
Explore how a bounded reliability workflow could connect evidence, engineering review, and an approved maintenance-system handoff.