AI for plants where downtime, scrap and lost expertise have a direct cost.
We help manufacturers use sensor data, images and decades of documentation to support maintenance, inspection and shift handover. Systems run where the plant needs them, including at the edge, and operators keep control of the line.
- Maintenance prioritization
- Defect detection support
- Shift handover summaries
- Supplier document checks
Workflows we work on
- 01
Condition-based maintenance
Vibration, temperature and runtime data from critical assets are monitored for patterns that preceded past failures, and planners receive a ranked list of assets to inspect with the evidence behind each flag.
- 02
Visual inspection at the line
Cameras capture parts at a defined station, a model flags likely defects by type, and an operator confirms or overrides each call, building a labeled record that improves the model over time.
- 03
Troubleshooting from manuals and work orders
Technicians describe a fault and get likely causes and steps drawn from equipment manuals, past work orders and engineering notes, with links to the original documents.
Constraints we design for
Operational technology boundaries
Plant networks are segmented for good reason. We read from historians and gateways through approved paths and do not write to control systems.
Edge and connectivity
Some inference must run on the line with limited bandwidth or none. We design for local processing, buffering and central model management.
Sparse failure data
Critical failures are rare, so models are built from normal operation, known failure modes and engineering knowledge, and are evaluated honestly against that limitation.
Operator trust
Recommendations explain themselves in the terms maintenance and quality staff use, and every override is captured rather than ignored.
Use cases we can explore
Where we start
Discovery begins with a few direct questions about your operation.
- 01Which asset or line has the most costly unplanned stops?
- 02What sensor, historian and maintenance data exists, and how far back does it go?
- 03Where are defects found today, and how are they recorded?
- 04Which expertise sits with a few experienced people who are hard to replace?
Relevant solutions and services
- Predictive MaintenanceUse equipment signals and maintenance history to rank which assets need attention, so maintenance teams can plan work before failures.
- Enterprise Knowledge AssistantAnswer staff questions from your own policies, procedures and records, with sources shown and access rules respected.
- Document IntelligenceClassify, extract, check and route incoming documents, with people reviewing the exceptions.
- Computer VisionImage and video models for inspection, counting, monitoring and visual document work, built for the conditions they will run in.
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- IoT & Edge SoftwareDevice connectivity, edge processing and data pipelines that turn equipment signals into records your teams and models can use.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
Questions buyers ask
No. It reads data and makes recommendations. Changes to equipment, schedules and quality dispositions are made by your staff through your existing systems.
Tell us the workflow you want to improve.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.