AI for Manufacturing Operations
Vision, assistants, agents and predictive models connected to your MES, ERP, CMMS and line equipment.
AI on the Plant Floor
Vision, assistants, agents and predictive models connected to the systems your plant already runs.
Visual Quality Inspection
Vision models that check parts on the line and log each defect with its image.
- Defect detection
- Image records
- Line cameras
Operator and Technician Assistant
Answers from SOPs, work instructions and maintenance history at the machine, with the source shown.
- SOPs
- Work instructions
- Cited sources
Maintenance Agents
Agents that open and route work orders from machine alarms and technician notes, with a supervisor approving.
- Work orders
- Approvals
- CMMS
Predictive Maintenance
Condition monitoring and failure risk for critical equipment from sensor and CMMS data.
- Condition monitoring
- Failure risk
- Spare parts
Production Planning
Demand and capacity forecasts that feed scheduling and material planning.
- Demand
- Capacity
- Scheduling
Quality Document Review
Supplier certificates, batch records and deviation reports read and checked against your requirements.
- Certificates
- Batch records
- Deviations
From Use Case to Production
Three phases. Each one ends with something your team can evaluate before the next begins.
Phase 1
Assess
We pick use cases with a clear business case, check data quality and access, and agree on how success is measured.
- Use cases ranked by value and risk
- Data and access review
- Success criteria and evaluation set
Phase 2
Build and Evaluate
We choose a model for each task, connect it to your data and systems, and test it against real cases before go-live.
- Model choice by task, cost and data residency
- Retrieval and system integrations
- Evaluations and guardrails
Phase 3
Deploy and Monitor
We release with human approval where it matters, monitor quality and cost in production, and extend to more sites and workflows.
- Approvals and audit trails
- Quality, cost and drift monitoring
- Rollout across sites
Across Manufacturing Sectors
Use cases adapt to the rules and processes of each sector.
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Automotive
Assembly line monitoring, visual inspection and supplier quality.
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Electronics
Defect detection and component traceability in precision assembly.
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Pharmaceuticals
Batch monitoring and quality records that support GMP requirements.
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Food and Beverage
Quality checks and safety monitoring that support HACCP programs.
AI for Manufacturing: Common Questions
What manufacturing teams ask before starting an AI project.
Contact our teamVisual inspection, an assistant grounded in SOPs and maintenance history, and predictive maintenance on critical equipment are common first projects.
Yes, through their APIs and standard industrial protocols such as OPC UA. Results land in the systems your teams already use.
It depends on the parts, lighting and defect types. We test against your own samples before go-live and keep a person in the loop for borderline cases.
It does not have to. Models can run on-premise or on edge hardware at the line, and sensitive data can stay out of third-party APIs.
We monitor accuracy and drift in production and retrain when products, suppliers or processes change.
We agree on a baseline and a target measure in the assessment phase, then test the pilot against it before scaling.
Discuss Your Manufacturing AI Use Case with Our Team
Tell us which lines or processes you want to improve. We will review your data and propose a first phase.
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01Discovery
We review your processes, systems and objectives.
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02Proposal
Defined scope, timeline and investment for phase one.
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03Delivery
A production release, with a roadmap to scale.