Capabilities / AI & Machine Learning
Artificial Intelligence & Machine Learning
Predict asset failures, optimize inventory, and automate maintenance decisions with industrial-grade AI.
We pair reliability engineering expertise with deep ML to deliver failure prediction models, spare parts optimization algorithms, and GenAI copilots that measurably reduce downtime and working capital.
From prior engagements
35%
Reduction in unplanned downtime
Average improvement in equipment availability through failure prediction models deployed on critical assets.
$12M+
Working capital released
Through ML-driven spare parts optimization and excess inventory identification on a single engagement.
3–15×
Return on engagement fee
In year one, from recoverable downtime and inventory. Not from headcount reductions.
How We Approach This
What makes this work.
The mechanics behind our engagements: what separates a diagnostic that produces a real business case from one that produces a slide deck.
Failure modes drive every model
Feature engineering starts with the failure mode, not the data. Criticality, operating regime, and maintenance history shape every model we build.
Predictions planners can act on
Explainable outputs that maintenance teams trust, not black-box scores nobody knows how to use. The model integrates with your work order workflow from day one.
Built for OT data reality
MLOps pipelines designed for sensor streams, historian feeds, and irregular maintenance logs, not the clean tabular data ML tutorials assume.
Adoption is part of the build
We design for the planner and technician experience from the start. A model nobody uses doesn't move any numbers.
What We Deliver
The work, scoped
and priced to move.
Each offering below is designed to produce a specific deliverable, not an ongoing program. We scope to outcome, not to time-and-materials.
Predictive Failure Modeling
Build and operationalize ML models that predict equipment failures before they cause unplanned downtime.
- –Failure mode and criticality analysis
- –Feature engineering from sensor and CMMS data
- –Remaining useful life (RUL) modeling
- –Alert and work order integration
Spare Parts & Inventory AI
Apply ML to right-size stock levels, eliminate excess and obsolete inventory, and reduce working capital.
- –Demand forecasting by failure probability
- –Reorder point and safety stock optimization
- –Excess & obsolete identification
- –MRP variable recommendations
Maintenance Planning Copilots
Deploy GenAI assistants that help planners write task lists, scope work orders, and surface relevant procedures.
- –RAG pipelines over technical documentation
- –Task list generation and validation
- –Work order scoping assistance
- –Procedure drafting and review
Anomaly Detection & Quality
Continuously monitor process and equipment data to surface deviations before they escalate.
- –Multivariate anomaly detection
- –Process deviation alerting
- –Root cause hypothesis generation
- –Closed-loop corrective action workflows
How we build it
Co-create AI programs anchored to failure modes, data availability, and maintenance workflows.
01
Discover & Prioritize
Map critical assets, failure modes, and data sources to build a value-ranked use case backlog.
02
Design & Pilot
Build feature stores, train models on CMMS and OT data, and validate predictions with maintenance teams.
03
Scale & Govern
Institutionalize MLOps, integrate with SAP PM/EAM, and track KPIs: MTBF, downtime, and inventory turns.
In Practice
Deployed failure prediction models across 120 critical assets and GenAI copilots that drafted maintenance task lists, cutting unplanned downtime by 34% and releasing $12M in excess inventory.
Outcome
$12M working capital released. Downtime cut 34%.
Find out which failures your data can already predict.
A diagnostic engagement runs 6–8 weeks and returns a prioritized roadmap with a dollar value on every initiative. If we can't see a clear path to 10× return on our fee, we won't take the work.