AI Agents
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, 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 reviews where commitments are logged the moment they're made
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
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
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
Compliance Manager 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 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
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
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
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, 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 |
| Compliance Manager | 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 |
| 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, 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
- 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.