Maintenance leaders
Fix before failure, not after.
Capability
Predictive Maintenance & Quality
Catch the failure and the defect before they reach the customer.
Unplanned downtimeMean time between failuresMaintenance cost per assetDefect escape rate
The service at a glance
The problem, our work, your result
- Business problem
Equipment fails without warning, and maintenance is calendar-based guesswork.
Unplanned stops cost far more than planned ones, and over-maintaining healthy assets wastes budget and time.
- What we do
We read vibration, temperature, pressure and process data to predict failures and quality drift early enough to act, and we schedule maintenance when the asset actually needs it.
- What you receive
- Failure prediction models
- Asset health scores
- Need-based maintenance scheduling
- Quality drift alerts
Who it is for
Built for the people who own the decision
Production
Fewer unplanned stops.
CFO and finance
Maintenance spend where it matters.
Quality
Drift caught before defects ship.
The goal: an AI-ready organisation
Where this service moves you
Data readyThis service
Connected, clean, governed and allowed for AI use.
KPIs readyThis service
Agreed measures, one definition each, a named owner.
Organisation ready
Roles, decision rights and skills to adopt AI safely.
=AI that reaches production and creates value
Approach
How we work with you
- 01
Instrument
Capture the right sensor and process data.
- 02
Learn
Train models on failure history.
- 03
Alert
Warn early with clear reasons.
- 04
Schedule
Plan work by asset condition.
Engagement options
Start where you are
Critical assets pilot
Predict failures on key equipment.
Plant-wide programme
All critical assets covered.
Reliability platform
Prediction plus scheduling integration.
Scope
What is in, what is not
Included
- Sensor data integration
- Prediction models
- Alerting and scheduling
- Maintenance system integration
Handled separately
- Sensor hardware supply
- Maintenance execution
Bilytica frameworks behind this service
Our own methods, not a generic playbook
DELIVER
All Bilytica frameworksAI Delivery Framework
A gated path from idea to live service, with evidence at every step.
See the frameworkDFFData Foundation Framework
The layers that turn scattered systems into data an AI system is allowed to use.
See the frameworkIn short
What this actually is
Condition monitoring and remaining-useful-life models on sensor, vibration, thermal and process data, plus visual inspection models on the line. Alerts arrive as work orders in the maintenance system with the evidence attached, so a planner can act without opening a data tool.
Key points
What matters before you buy
- 01
Failure history decides feasibility
Without labelled failures there is no model, we start with anomaly detection and build the label set as you run.
- 02
Alerts must become work orders
An alert in a dashboard nobody owns changes nothing; it belongs in the CMMS queue with a priority.
- 03
False alarms cost trust fast
Thresholds are tuned with the maintenance team, and we report precision honestly rather than optimising for recall alone.
- 04
Vision inspection catches what sampling misses
Every unit checked instead of a sample, with defect images retained for the root-cause discussion.
Delivery
How we build it
Foundation check
Before any buildWe inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.
Narrow first release
First releaseOne workflow, one team, real traffic, measured against the baseline we captured before we started.
Controls and evaluation
Runs in parallelGuardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Scale and hand over
After sign-offAdditional workflows, sites or languages, then runbooks and training so your team can operate it without us.
Data foundation
What has to be true in your data first
Great AI cannot hide bad data. These are the conditions this capability depends on, we fix them as part of delivery, not after go-live.
- Time-series sensor data at a sampling rate that can actually show the failure mode.
- Maintenance history with real failure codes, not 'other'.
- Asset criticality so alerts can be prioritised.
- Labelled defect images for any visual inspection model.
Controls
Guardrails we put around it
Every action is logged and replayable
Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.
Accuracy is monitored, not assumed
A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.
A human owns every consequential decision
Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.
Data stays where your regulator requires
In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.
Fit
Systems it plugs into, and what it moves
Integrations
SAP PMIBM MaximoOracle EAMOSIsoft PIEdge inference hardwareSCADA historians
KPIs it moves
- • Unplanned downtime
- • Mean time between failures
- • Maintenance cost per asset
- • Defect escape rate
- • Alert precision and false-alarm rate
By industry