Predictive maintenance
Failure prediction from sensor and maintenance data, tied into work-order execution.
Industry
AI for Manufacturing
Reduce downtime and defects with AI connected to OT, quality systems, and planning.
- 37%unplanned downtime reduction
- 25%quality-escape reduction
- 15%OEE improvement on focus lines
Capabilities
AI from shop floor to supply chain
Predictive maintenance, quality inspection and supply chain AI for manufacturers.
Quality AI
Defect detection and root-cause analytics across lines, suppliers and shifts.
Supply chain visibility
Disruption sensing and inventory intelligence across tiers.
Ops copilots
Operator and engineer assistants grounded in manuals, history and telemetry.
How we deliver in manufacturing
OT-aware AI that plants actually adopt
Manufacturing AI dies in the IT/OT gap and the pilot trap. We bridge the first and engineer past the second.
Reliability
Maintenance that arrives before the failure
Models trained on your sensor, alarm and work-order history predict failures with lead time to act — and push prioritized work orders into your CMMS, closing the loop.
- Failure prediction with actionable lead times
- Work orders raised in your CMMS automatically
- Technician copilots with manuals and history
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Quality
Defects caught at the line, causes found across it
Inline detection plus cross-line analytics that connect defects to suppliers, parameters and shifts — so you fix causes, not just catch symptoms.
- Vision and sensor-based inline detection
- Root-cause analytics across process parameters
- Supplier quality scorecards from real outcomes
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Knowledge
Thirty years of tribal knowledge, on demand
Manuals, SOPs, maintenance logs and retired-expert knowledge made queryable at the line — new operators perform like veterans, escalations drop.
- Operator Q&A grounded in your documentation
- Guided troubleshooting from historical fixes
- Multilingual access on shop-floor devices
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
Ask anything…
Use cases
Where manufacturers deploy AI with us
Predictive maintenance
Downtime prevented on critical assets with CMMS integration.
Quality inspection
Inline defect detection with root-cause analytics.
Operator assistance
SOP and troubleshooting copilots at the line.
Supply disruption sensing
Multi-tier risk signals with mitigation options.
Production scheduling
Constraint-aware schedule optimization with explainability.
Energy optimization
Consumption analytics and setpoint recommendations.
Delivery
Industry-first delivery
Every engagement is grounded in your domain — regulations, operating models, and the use cases that actually move the needle.
- Domain-ready use cases
- Compliance-aware design
- Ops & CX impact
- Reusable industry playbooks
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1
Map the domain
Identify high-ROI workflows and risk boundaries.
-
2
Prioritize use cases
Score impact, feasibility and data readiness.
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3
Pilot with guardrails
Ship a controlled production pilot fast.
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4
Scale across ops
Expand to adjacent processes with shared platforms.
Two production lines, 37% less unplanned downtime in nine months. The part that surprised us: operators actually use the copilot — because it answers from our manuals, not the internet.
Works with your stack
Bridges your IT and OT
Historians, MES, CMMS and ERP integrated with OT-safe patterns — read-only where required, actioned where trusted.
- OSIsoft PI
- Ignition
- SAP
- Maximo
- Rockwell
- Siemens
- Azure IoT
- AWS
- Databricks
- Anthropic Claude
- OpenAI
- Kubernetes
FAQ
Common questions
How do you get past the pilot trap?
By designing for scale on day one: data pipelines that generalize across lines, integration with CMMS/MES rather than dashboards nobody checks, and ROI measured per line so expansion is a finance decision, not a leap of faith.
Is our OT data safe?
We follow OT security patterns: read-only historian access by default, no direct control-system writes without engineered safety layers, and network segmentation respected throughout.
Our data is messy — years of inconsistent logs. Still viable?
Almost always. Messy maintenance logs and inconsistent tags are the norm; the ingestion layer we build handles cleaning and entity resolution, and value starts with whichever assets have the best signal.
Cloud or on-prem for plant AI?
Hybrid is typical: edge inference where latency matters, plant-level processing on-prem where connectivity is constrained, cloud for training and fleet analytics. We design to your topology.
Next step
Make every line your best line
Pick a line and a loss category — downtime, defects or throughput — and we'll target it with a measurable pilot.