Solutions · Use case
Predictive Maintenance.
Generative AI, scoped to the shape of the problem it solves.
Lead-time-to-failure prediction over historian, sensor, and incident-log data.
Use-case context
Asset-heavy industries lose disproportionate margin to unplanned downtime, and the data needed to predict it — historian streams, vibration sensors, maintenance logs, incident reports — already exists in their systems. The hard problem is not the model: it is calibrating predictions against ground-truth incident logs so that lead-time-to-failure estimates are actionable rather than alarmist. We build prediction pipelines that combine time-series models over sensor data with NLP over incident-report and maintenance-log corpora, surface predicted failures with a lead-time band and a confidence score, and route the top-ranked alerts into the existing CMMS or work-order queue. The model is observable and re-trainable as new ground-truth incidents arrive. Measured by precision-at-k on lead-time-to-failure predictions calibrated against ground-truth incident logs (top-k predicted failures per week, scored against what actually happened), plus mean-time-to-detect (MTTD) on incidents the model surfaces. False-positive rate is tracked because every false alarm costs a maintenance crew dispatch. We do not deploy predictive-maintenance outputs into control-room or asset-safety systems autonomously — predictions feed planners and reliability engineers, not the SCADA layer or the protective-relay logic. In our coverage footprint this lands first across energy, manufacturing — the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.
How this shows up across industries
Where predictive maintenance lands in production engagements.
Energy · Data Science & Analytics
See industry →In energy, data science covers asset-failure prediction, demand forecasting, and operational-cost optimisation.
Manufacturing · Data Science & Analytics
See industry →In manufacturing, data science targets predictive maintenance, quality-defect prediction, and yield optimisation.
Data shape
Historian time-series streams, vibration and condition-monitoring sensors, maintenance work-order history, incident reports, and the asset register that ties identifiers across systems.
Where it lands first
Industries that include predictive maintenance in their applicable use-cases.
Where we draw the line
We do not deploy predictive-maintenance outputs into control-room or asset-safety systems autonomously — predictions feed planners and reliability engineers, not the SCADA layer or the protective-relay logic.
Talk to us about a predictive maintenance engagement
A 30-minute call to scope where predictive maintenance actually moves the curve against your evaluation criteria.
Book strategy callWhy work with Veso AI on predictive maintenance
Measured
Evaluation, not opinion
Measured by precision-at-k on lead-time-to-failure predictions calibrated against ground-truth incident logs (top-k predicted failures per week, scored against what actually happened), plus mean-time-to-detect (MTTD) on incidents the model surfaces. False-positive rate is tracked because every false alarm costs a maintenance crew dispatch.
Fixed-fee
After paid discovery
Two-week discovery assembles the labelled evaluation set with your subject-matter experts, then converts into a fixed-fee proposal with explicit gates.
Your repo
Your IP, day one
Code, infrastructure-as-code, evaluation harness, and runbooks land in your accounts — no vendor lock-in on the data, models, or evaluation history.
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FAQ
Predictive Maintenance — frequently asked questions
How is success measured for predictive maintenance engagements?
Measured by precision-at-k on lead-time-to-failure predictions calibrated against ground-truth incident logs (top-k predicted failures per week, scored against what actually happened), plus mean-time-to-detect (MTTD) on incidents the model surfaces. False-positive rate is tracked because every false alarm costs a maintenance crew dispatch. The evaluation harness is part of the deliverable, not an afterthought — we build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.
Where does Veso AI NOT apply predictive maintenance?
We do not deploy predictive-maintenance outputs into control-room or asset-safety systems autonomously — predictions feed planners and reliability engineers, not the SCADA layer or the protective-relay logic. This is a deliberate trust boundary, not a capability gap — we are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.
Which industries does predictive maintenance apply to?
In our coverage footprint, predictive maintenance most commonly lands in energy, manufacturing. The specific deployment shape varies by industry — data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.
What data shape do you need to start a predictive maintenance engagement?
Historian time-series streams, vibration and condition-monitoring sensors, maintenance work-order history, incident reports, and the asset register that ties identifiers across systems. During the paid two-week discovery we map the actual data surface — what exists, what is labelled, what residency posture it carries — and the proposal for the next gate is shaped against that, not against an assumption.
Which Veso AI services ship predictive maintenance?
predictive maintenance ships under our Generative AI Consulting, Custom Software Development, Cloud Solutions Architecture, Data Science & Analytics service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one — the boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.
How does a predictive maintenance engagement typically start?
With a paid two-week discovery: workshops with leadership and operators, an evaluation-set assembled with your subject-matter experts, and a fixed-fee proposal for the next gate. The evaluation set anchors every subsequent decision — model choice, prompt strategy, retrieval design — so quality is measurable from week one, not from go-live.
Industries where predictive maintenance applies
Service lines that ship predictive maintenance