Facilities Predictive Analytics Development

Facilities analytics for one maintenance decision the current systems cannot support.

Predictive facilities work starts with a specific asset, failure or degradation target, intervention horizon, and maintenance decision. We build focused analytics and workflow extensions over approved BMS, sensor, CMMS, work-order, and asset data. When the evidence is not ready for prediction, monitoring and preventive-maintenance workflow are the honest first release.

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

Evidence and scope

10 to 16 weeks

First release

One asset class and one monitoring or maintenance decision.

$40K

Starting scope

Data proof, baseline, alert or score, CMMS handoff, and evaluation.

Fixed price

Commercial model

Scope and price agreed before development starts.

Evidence · planning contextSee the work

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Are building alarms, sensor trends, asset history, work orders, and technician findings separated when maintenance decides what to inspect?

02

Is a predictive-maintenance proposal promising failure forecasts without representative labelled history or a response workflow?

Plain answer

Facilities management predictive analytics combines building, sensor, asset, work-order, and maintenance data to support a defined inspection or intervention decision. RaftLabs builds focused monitoring and predictive workflows over existing BMS and CMMS systems. First releases start around $40,000; prediction is scoped only when representative historical evidence supports reliable evaluation.

The alarm fired. The work order closed. The model never learned what happened.

The BMS recorded a high temperature, a technician replaced a component, and the CMMS closed the task under a generic reason. Six months later, the same pattern appears, but the systems still cannot connect signal, asset, intervention, and outcome.

Prediction requires that connection. Before a model, define the decision, intervention horizon, evidence, baseline, and response. A dependable threshold with an owned work-order path is more useful than an impressive score nobody can validate.

Delivery record

Since 2015
shipping production software
RaftLabs delivery record
4.9/5
average client rating
Clutch, verified reviews
8 weeks
post-launch support included
Every RaftLabs engagement

RaftLabs does not publish a named facilities-predictive-maintenance case study. These company-wide facts are not evidence of failure reduction, energy savings, or maintenance return. Any such outcome needs a defined baseline, measurement window, and client-controlled analysis.

Prediction fits one asset decision with representative evidence and a response owner.

Monitoring or preventive workflow is the right first release when labels and outcomes are weak.

A fit
01

A bounded asset class has accessible signals, maintenance history, stable identity, and a costly recurring decision.

02

The team can define the target event, useful prediction horizon, intervention, false-alert cost, baseline, and reviewer.

03

Facilities, engineering, maintenance, security, and technology owners can validate data, model use, response, and operation.

Not a fit
01

The real need is preventive scheduling, work-order discipline, asset records, or threshold monitoring.

02

Failure labels, maintenance outcomes, asset mappings, or sensor quality are too weak for representative evaluation.

03

The project expects the model to approve safety decisions or control equipment without an engineering-owned authority path.

Monitoring, CMMS, or predictive analytics?

A CMMS owns assets, maintenance plans, work, labour, parts, and history. Monitoring owns current signals and alerts. Predictive analytics estimates a defined future event or priority from evidence. Many facilities need stronger identity, monitoring, and work-order outcomes before a model can add value.

Monitoring and preventive workflow vs prediction

Monitoring or CMMS workflowPredictive analytics
QuestionWhat is happening, what crossed a rule, and what work is due?What is likely to happen within a useful intervention horizon?
EvidenceCurrent signals, thresholds, assets, plans, and work ordersRepresentative historical features, targets, outcomes, and a baseline
ValidationFreshness, accuracy, alert, workflow, and reconciliation testsTime-aware evaluation of misses, false alerts, lead time, drift, and operations
First releaseBest when data or labels are immatureJustified when prediction changes a recurring maintenance decision
Main riskAlarm noise and poor follow-upA model that learns leakage, seasonality, or maintenance recording habits

Scope

What belongs in a credible facilities analytics release

  • 01
    Asset and target definition
    Choose one asset class, failure or degradation event, decision, horizon, intervention, owner, baseline, and cost of missed or false alerts. Keep safety and engineering authority outside the model.
  • 02
    Building and maintenance data
    Map asset identity across BMS, sensors, historian, CMMS, work orders, inspections, weather, occupancy, and energy sources. Preserve units, timestamps, quality, source, maintenance windows, replacements, and configuration changes.
  • 03
    Baseline and evaluation
    Compare the proposed model with a simple rule, schedule, or current practice. Split data by time and asset, prevent future information from leaking into training, inspect rare events, and report lead time, misses, false alerts, and cohort performance.
  • 04
    Review and maintenance handoff
    Present the signal, contributing evidence, uncertainty, asset context, and recommended review. Let authorised facilities staff accept, reject, defer, or annotate it, then create or enrich CMMS work through a controlled interface.
  • 05
    Model and data operations
    Monitor feeds, mappings, quality, feature distribution, prediction volume, drift, alert outcomes, response, cost, and model versions. Define retraining evidence, approval, rollback, incident handling, and retirement.

How it works

From asset decision to monitored maintenance workflow

  1. Phase 1
    01

    Bound asset and decision

    Select one asset class, target event, useful horizon, intervention, users, baseline, data sources, safety boundary, operating owner, and acceptance measures.

  2. Phase 2
    02

    Prove data and baseline

    Test representative normal, degraded, failed, maintained, missing, stale, seasonal, changed-asset, and CMMS-link cases against a simple baseline.

  3. Phase 3
    03

    Build the decision workflow

    Deliver ingestion, asset mapping, features, monitoring or model, evaluation, alert review, CMMS handoff, observability, history, and tests.

  4. Phase 4
    04

    Release and monitor evidence

    Start in shadow or advisory mode, compare decisions and outcomes, tune thresholds, monitor drift, and expand only after the first asset path holds.

Risk

What the prediction score can hide

Future-data leakage
Build features only from information available at prediction time and evaluate on later periods and unseen assets.
Maintenance records are weak labels
Separate failure, inspection, preventive replacement, repeated visit, and generic closure. Review ambiguous outcomes with domain owners.
Assets change underneath the model
Track replacements, sensor moves, firmware, setpoints, controls, seasons, occupancy, and maintenance so drift is visible.
No response or safety boundary
Define who reviews, inspects, intervenes, defers, and records outcome. The model supports maintenance judgment; it does not authorise safe operation.

Scope and price

A focused facilities analytics release starts at $40,000.

Start with one asset class, one maintenance decision, a data and baseline proof, monitoring or a bounded model, CMMS handoff, evaluation, and ownership.

This is an indicative starting point, not a quote or failure-reduction guarantee. Prediction is priced only after target, evidence, evaluation, response, and safety boundaries are approved.

Starting investment

Starts at $40,000

A focused release usually takes 10 to 16 weeks. More buildings, edge systems, sensors, models, sparse failures, or safety-relevant decisions add work.

A simple baseline comes first

The model must beat a defined rule, schedule, or current practice on representative time-aware evidence.

Maintenance ownership ships with analytics

Eight weeks of support are included with data, model, alert, CMMS, drift, incident, and change runbooks.

Related facilities and analytics services

Facilities predictive analytics questions

It uses approved building, sensor, asset, work-order, maintenance, weather, occupancy, or energy data to support a defined facilities decision. That may be anomaly detection, inspection priority, fault classification, or maintenance timing. A live dashboard or threshold alert may be sufficient without a predictive model.

A useful project needs stable asset identity, a path for current signals, and records of maintenance decisions or outcomes. Prediction also needs representative history for the target event and horizon. If that evidence is missing, begin with monitoring, data quality, and CMMS workflow rather than promising forecasts.

No system can guarantee prevention. Analytics may identify patterns worth inspection or intervention, but misses, false alerts, unseen failure modes, maintenance quality, parts, weather, occupancy, and human response affect outcomes. Facilities and engineering owners decide what to inspect, repair, defer, or shut down.

A first release starts around $40,000 for one asset class, source audit, asset mapping, baseline, monitoring or bounded model, evaluation, alert review, one CMMS handoff, observability, and handover. More buildings, sensors, edge systems, models, integrations, or safety-relevant use add scope.

A focused release usually takes 10 to 16 weeks after data access, asset mapping, maintenance outcomes, target definition, and reviewers are ready. Sensor repair, BMS vendor access, sparse failures, seasonal evaluation, several sites, security review, and CMMS data quality can extend the plan.

Work with us

Bring one facility asset decision that current alarms cannot support.

We will assess the target, data, baseline, response, and CMMS path, then tell you whether monitoring or prediction is justified.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.