AI Agents for Manufacturing
Every shift generates decisions, problems, and answers that shouldn't have to be found twice. Our AI Agents capture them, connect them, and put them to work across every plant.
Turn Daily Meetings into Daily Execution
Meeting Sense transforms Tier, SIM, SQDIP or SQCDP, and operational meetings into structured execution systems. It automatically captures decisions, actions, owners, deadlines, and commitments, ensuring that critical follow-up activities are tracked, executed, and verified. Instead of relying on meeting notes and manual follow-up, teams gain accountability, visibility, and faster issue resolution across every shift and every plant.
- Shift handoff with every open action carried forward and owned
- Tier meeting follow-through across plants, with deadlines tracked automatically
- SQDIP or SQCDP reviews where commitments are logged the moment they're made
See it in:LOOP tier meetings
Instantly Search Every Decision Ever Made
Meeting Insight gives leaders the ability to instantly search and analyze discussions, decisions, actions, and outcomes across the entire enterprise. Whether investigating recurring issues, reviewing operational performance, or onboarding new team members, Meeting Insight turns years of meeting knowledge into an accessible and searchable operational intelligence asset.
- New plant manager onboarding — months of context in minutes
- Recurring issue investigation across shifts, lines, and sites
- Quarterly operational reviews backed by what was actually said and decided
See it in:LOOP tier meetings
Find Proven Solutions Before Problems Escalate
Fix Finder transforms institutional knowledge into a reusable enterprise asset. When issues occur, it automatically searches past incidents, corrective actions, root causes, and successful resolutions across all plants and teams, helping operators and managers solve problems faster, reduce downtime, and avoid repeating mistakes.
- Recurring downtime — surface the fix that already worked on a sister line
- Cross-plant lookup for the same fault code or quality defect
- Faster MTTR by routing the proven corrective action to the operator on shift
See it in:Agentic MESFishbone diagram template
Ask Anything. Find Everything.
Deep Find enables employees to instantly search Standard Operating Procedures, work instructions, manuals, engineering documents, maintenance records, quality reports, and operational data using natural language. Instead of spending hours searching multiple systems, teams can find the information they need in seconds and make better decisions faster.
- SOP lookup mid-shift without leaving the line
- Engineering document search across legacy and current revisions
- Quality record retrieval during a customer complaint or audit
Ensure Every Process Follows Your Standards
Process Conformance continuously monitors operations against company-defined procedures, quality requirements, and operational standards. It automatically identifies deviations, alerts responsible teams, and tracks corrective actions, helping manufacturers improve consistency, strengthen governance, and maintain continuous audit readiness.
- Real-time deviation alert with the corrective action already attached
- Audit prep with a continuous, time-stamped record of conformance
- Standard-work governance across every shift and every site
See it in:ADEO case study
Better Fault Descriptions, Written the First Time
Issue Coach works alongside the operator at the moment an issue is logged. It checks the fault description as it is being written — who, what, where, when, how — and asks for whatever is missing, so every issue enters the system complete enough to act on and to reuse later.
See it in:Agentic MESSupervisors
From Stop to Task in One Step
Task Pilot removes the gap between seeing a stop and acting on it. From the stop page itself, it opens a task or a maintenance call and carries the stop context with it — machine, time, cause, and description — so nothing has to be re-typed and nothing gets lost.
See it in:Agentic MESSupervisors
The Shift Handover, Written for You
Shift Pilot drafts the shift handover automatically — stops, losses, and open actions — so the operator corrects a draft instead of writing from memory at the end of a long shift. The next shift starts from what actually happened, not from what anyone remembers.
See it in:Agentic MESSupervisors
See Asset Risk Before It Becomes Downtime
Asset Health Agent continuously monitors condition, operating behavior, and performance signals from the assets that matter most. It unifies data from SCADA, historians, sensors, EAM/CMMS, and operational systems to build a live picture of asset health, flag anomalies, and surface emerging risk before it turns into unplanned downtime.
- Early warning on bearing degradation, vibration trends, or thermal drift
- Unified health score across assets, sites, and fleets
- Prioritized watch list based on criticality and operating context
See it in:Predictive MaintenanceAsset Performance Management
Find the Real Cause, Not Just the Symptom
Reliability & Root Cause Agent turns asset data and operational history into actionable reliability intelligence. It predicts degradation trajectories, identifies the most likely failure modes and root causes, and sizes the operational and business impact so teams know where to focus and why.
- Degradation prediction with ranked likely causes and confidence
- Business-impact sizing for availability, cost, and safety risk
- Cross-asset pattern detection for systemic reliability issues
See it in:Predictive MaintenanceAsset Performance Management
Recommend the Next Best Action, Not Just the Next Alert
Prescriptive Maintenance Agent goes beyond prediction to recommend the best response. It weighs asset history, engineering knowledge, risk, cost, parts availability, technician skills, and production constraints to propose the right action at the right time — and feeds it directly into the work coordination loop.
- Recommended action with parts, people, and timing attached
- Risk-cost trade-off analysis for repair, replace, or run decisions
- Preserved engineering rationale for the next similar event
See it in:Predictive MaintenanceAsset Performance Management
Each agent draws from a different source of operational truth — meetings, incidents, documents, live performance, and asset condition — so together they cover what happened, what's already been solved, what the procedure says, whether you're still on standard, and which assets need attention next.
| Agent | Problem it solves | Primary data source | Primary user |
|---|---|---|---|
| Meeting Sense | Decisions and actions from daily meetings get lost or never followed through | Live capture from Tier, SIM, SQDIP or SQCDP, and operational meetings | Shift leaders and frontline teams |
| Meeting Insight | No way to search or analyze what's been decided across the enterprise over time | Historical meeting records and transcripts | Plant and enterprise leadership |
| Fix Finder | Teams re-investigate the same root causes because past fixes aren't easy to find | Incident records, corrective actions, root cause history | Operators, maintenance and quality managers |
| Deep Find | Hours lost searching SOPs, manuals, and records across multiple disconnected systems | SOPs, work instructions, manuals, engineering and quality documents | All employees |
| Process Conformance | Deviations from standard go unnoticed until they surface as audit findings | Live operations data measured against defined procedures and standards | Quality, compliance, and plant management |
| Issue Coach | Fault descriptions arrive incomplete, so investigations start with a phone call instead of the facts | Issue and fault descriptions entered by operators on the floor | Operators and shift leaders |
| Task Pilot | Stops get noted but the follow-up task or maintenance call never gets created | Live stop and downtime records | Operators and maintenance teams |
| Shift Pilot | Shift handovers depend on memory, so stops and open actions get lost between shifts | Stops, losses, and open actions recorded during the shift | Operators and shift supervisors |
| Asset Health Agent | Condition data is scattered across systems; emerging risk is missed until it becomes a failure | SCADA, historians, sensors, IoT, EAM/CMMS, and operational systems | Reliability engineers and maintenance managers |
| Reliability & Root Cause Agent | Teams react to symptoms because the real cause is buried across data silos and expert knowledge | Asset history, maintenance records, failure modes, operating context, and engineering knowledge | Reliability engineers and root-cause analysts |
| Prescriptive Maintenance Agent | Predictions stop at alerts; teams still have to decide what to do, when, and with what resources | Asset history, engineering knowledge, risk models, cost data, and operating constraints | Maintenance planners and operations managers |
- Problem
- Decisions and actions from daily meetings get lost or never followed through
- Data source
- Live capture from Tier, SIM, SQDIP or SQCDP, and operational meetings
- Primary user
- Shift leaders and frontline teams
- Problem
- No way to search or analyze what's been decided across the enterprise over time
- Data source
- Historical meeting records and transcripts
- Primary user
- Plant and enterprise leadership
- Problem
- Teams re-investigate the same root causes because past fixes aren't easy to find
- Data source
- Incident records, corrective actions, root cause history
- Primary user
- Operators, maintenance and quality managers
- Problem
- Hours lost searching SOPs, manuals, and records across multiple disconnected systems
- Data source
- SOPs, work instructions, manuals, engineering and quality documents
- Primary user
- All employees
- Problem
- Deviations from standard go unnoticed until they surface as audit findings
- Data source
- Live operations data measured against defined procedures and standards
- Primary user
- Quality, compliance, and plant management
- Problem
- Fault descriptions arrive incomplete, so investigations start with a phone call instead of the facts
- Data source
- Issue and fault descriptions entered by operators on the floor
- Primary user
- Operators and shift leaders
- Problem
- Stops get noted but the follow-up task or maintenance call never gets created
- Data source
- Live stop and downtime records
- Primary user
- Operators and maintenance teams
- Problem
- Shift handovers depend on memory, so stops and open actions get lost between shifts
- Data source
- Stops, losses, and open actions recorded during the shift
- Primary user
- Operators and shift supervisors
- Problem
- Condition data is scattered across systems; emerging risk is missed until it becomes a failure
- Data source
- SCADA, historians, sensors, IoT, EAM/CMMS, and operational systems
- Primary user
- Reliability engineers and maintenance managers
- Problem
- Teams react to symptoms because the real cause is buried across data silos and expert knowledge
- Data source
- Asset history, maintenance records, failure modes, operating context, and engineering knowledge
- Primary user
- Reliability engineers and root-cause analysts
- Problem
- Predictions stop at alerts; teams still have to decide what to do, when, and with what resources
- Data source
- Asset history, engineering knowledge, risk models, cost data, and operating constraints
- Primary user
- Maintenance planners and operations managers
One plant. One use case. Real data.
Clear success criteria. Walk away on day 14 if it doesn't move the number.