Reliability engineering platform

From failure data to maintenance decisions.

Reliafy turns your reliability data into fitted life models, reliability block diagrams, and cost-optimal maintenance strategies — all in one place.

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Life-distribution modelling

Fit Weibull, Lognormal, Exponential, Gamma, Normal, and proportional-hazards models to exact, censored, and truncated failure data. Probability plots with confidence bounds, goodness-of-fit, and a survival calculator.

Reliability block diagrams

Wire components in series, parallel, k-of-n, and standby on a drag-and-drop canvas to compute system reliability, MTTF, importance measures, and minimal path/cut sets.

Maintenance strategy

Rank every candidate distribution against your data, compare two designs head-to-head, and find the cost-optimal preventive-replacement interval.

Degradation & remaining useful life

Fit per-unit wear paths to a failure threshold, then track in-service items and predict when each will cross it — with credible intervals that tighten as inspections come in.

Evidence-linked RCM

Build Function → Failure → Mode worksheets where every maintenance decision cites the analysis that justifies it — and gets re-checked live when the data changes.

Your data, organised

Upload a CSV once and reuse it across models. Saved models reopen instantly, and everything is private to your account.

Reliability Centred Maintenance

Maintenance decisions that prove themselves.

Every decision in an RCM study links to the analysis behind it: a run-to-failure call cites the life model that shows failures are random, a fixed interval cites the cost-optimal replacement analysis, condition monitoring cites the degradation model.

And the links stay live — when new data shows a "random" failure mode is actually wearing out, the study flags the decision as contradicted instead of letting it quietly go stale.

An RCM worksheet with live evidence statuses, including a contradicted run-to-failure decision

Degradation & RUL

Know when it will fail — before it does.

Your inspection sheets already contain the failure dates of equipment that hasn't failed yet. Fit a degradation model from historical wear data, register the items you're running today, and get a remaining-useful-life estimate for each one.

Every new reading tightens the prediction. Health badges turn from healthy to plan-replacement to replace-now while there's still time to act.

A tracked item's remaining-useful-life outlook with a 95% credible band and predicted threshold crossing

Work as a team

Shared team workspaces where everyone sees the same models, studies, and diagrams. Viewers are free; editing comes with Pro. Or share a single analysis, view-only, with anyone.

An assistant that does the work

Ask in plain language and the assistant acts in the app: fits models, builds RBDs, runs replacement calculations, tracks degradation, and drafts RCM studies — with your data, on your screen.

Open source at the core

The full toolkit is AGPL-licensed and self-hostable with one command. Your data can stay on your hardware; the cloud adds accounts, teams, and the assistant.

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