Breakdown is already late
Reactive maintenance tells you what failed. Asset-health intelligence is useful when it gives you enough warning to plan interventions during normal stops.
NIMBUSASSET HEALTH INTELLIGENCE
Western Cape · customer discovery & pilot stage
NIMBUS / ASSET HEALTH INTELLIGENCE
Catch equipment failure before it stops your line.
NIMBUS turns existing PLC tags, maintenance history, and targeted condition sensing into early breakdown warnings—starting with a focused 4-week assessment, not an expensive plant-wide IoT rollout.
No upfront sensor hardware required. We start with your operating problem and the evidence already available.
ILLUSTRATIVE ASSESSMENT VIEWPRE-PILOT
Candidate assetCritical pump P-04
HIGH CRITICALITYFailure consequenceProduction interruption
Existing evidenceMaintenance + current
Condition gapVibration baseline
Recommended next stepEstablish operating baseline before alerting
Registered SA private company
Founder-led data engineering
Western Cape, South Africa
Discovery → assessment → pilot
THE OPERATING PROBLEM
Most plants do not need more dashboards.
They need a sharp answer to three questions: which 3 to 5 assets matter most, what failure evidence already exists in their data, and what signal would actually change a maintenance decision.
Useful data is fragmented
Maintenance logs, PLC tags, operator observations, and sensor readings live in separate silos. Real value starts by connecting them around the asset.
Blanket sensing wastes capital
Instrumenting every motor creates noisy dashboards and ignored alerts. Asset criticality and failure modes must determine what gets measured.
WHAT NIMBUS DOES
Build condition intelligence around decisions.
We map asset criticality and failure modes first, then introduce sensing or analytics only where it closes a genuine operational decision gap.
Breakdown is already late
Reactive maintenance tells you what failed. Asset-health work is useful when it creates enough warning to move work into a planned window.
Blanket sensing wastes capital
Placing sensors on every asset before mapping failure modes leads to noisy dashboards and ignored alerts. We start with the critical few.
Evidence precedes investment
We validate condition logic against historical data and low-cost baselines before recommending permanent sensor or software deployment.
A STAGED COMMERCIAL MODEL
Evidence before scale.
01
Discovery
20 min + optional 2 weeksStart with a free 20-minute discovery conversation. A paid two-week Discovery is used only when scope, evidence or access are not yet mature enough for Assessment.
02
Asset Health Assessment
4 weeksBuild an evidence-based view of criticality, condition signals and the monitoring gaps worth addressing.
03
Pilot
6–8 weeksValidate a bounded monitoring workflow on a small asset set, with success criteria agreed before deployment.
04
Continuous Monitoring
OngoingScale only after the pilot demonstrates useful evidence in the customer’s own operating context.
EVIDENCE, NOT THEATRE
Start interpretable. Earn complexity.
Initial work focuses on baselines, trends, thresholds, spectral features, and clear asset context. Machine learning algorithms are applied only when operating data and repeated regimes justify them.
Condition monitoring logic is validated against operational benchmarks and customer-specific thresholds calibrated directly from machine run regimes.
Existing data firstPLC / SCADA tags, Historians, maintenance records, and operator logs.
Lightweight sensing where justifiedVibration, temperature, current / power, or process variables.
Calibrated thresholdsCondition alerts are interpreted within agreed operational envelopes.
STARTING LOCAL
Stellenbosch → Somerset West → Western Cape.
NIMBUS begins with reachable Western Cape engineering teams where site walkthroughs and evidence reviews happen quickly. Our focus is delivering one strong assessment and one documented operating success before broad expansion.
FOUNDER & TECHNICAL LEADERSHIP
Engineered by a quantitative data pipeline specialist.
NIMBUS is founder-led by Sergio Matolla, combining quantitative analytics, high-throughput data engineering credentials, and industrial reliability principles.
Sergio MatollaCape Town, SA
Business & Data Engineering professional specializing in large-scale automated data pipelines, multivariate time-series anomaly detection, and cryptographic systems.
15M+ Record ETL ScaleSecurity & Vulnerability Auditing
TECHNICAL TRACK RECORD & CREDENTIALS
- High-Throughput ETL: Engineered pipelines processing 15M+ transaction records with zero data loss.
- Algorithmic Anomaly Detection: Time-series autoencoder & multivariate graph attention models for complex data.
- Direct Founder Oversight: Every Discovery session & Assessment report is personally audited by Sergio.