Reliability centred maintenance, backed by your failure data

Decisions that cite their evidence

Most RCM tools are structured note-taking: the worksheet records that the team chose a fixed 6-month interval, but not why, and nothing ever checks the why again. In Reliafy, each decision links to the analysis behind it — a run-to-failure call cites the fitted life model showing failures are random; a fixed interval cites the cost-optimal replacement analysis; an on-condition task cites the degradation model; a failure-finding interval cites the availability calculation.

Contradicted, not quietly stale

The links stay live. When new failure data shows a "random" failure mode is actually wearing out, the study flags that decision as contradicted — with the evidence one click away — instead of letting the worksheet drift out of date for five years. Each study rolls up how many decisions are supported, unverified, or contradicted, so an audit starts from a dashboard rather than a binder.

The classic structure, without the ceremony

Worksheets follow the standard decomposition — functions, functional failures, failure modes with effects and consequences, then the maintenance decision. Mode-level fields keep the analysis honest (what does failure look like, how is it detected), and the whole worksheet exports to CSV for reports and audits.

Built on real analyses, in the same tool

The evidence isn't imported from somewhere else — you fit the life models, replacement intervals, and degradation models in the same platform, from the same datasets. Teams work in a shared workspace, and single studies can be shared view-only with anyone, referenced evidence included. Walk through a complete worked RCM example — a pump system, five failure modes, every decision reasoned — or start from the built-in sample study.