AMI
The AI layer inside SmartWorker. Ask your production data a question. Get a specific, evidence-backed answer.
Experienced supervisors carry institutional knowledge that takes years to build: which machine causes downstream stoppages, which SKU destabilises a specific filler, which crew consistently recovers faster after a fault. AMI makes that knowledge available to every team member, systematically and in real time.
It is not a generic AI assistant. Every response is grounded in Navigator’s production data, live line events, historical loss patterns, previous actions and their outcomes. When AMI says the palletiser is the primary constraint, it shows the 4.44% loss data behind it.
AI Chat — Instant Production Q&A
Ask any operational question. AMI reads across live loss data, event history, and connected documents to give an answer with evidence.
Loss Pattern Interpretation
AMI analyses causal chains, not just individual events. When a filler is faulting, it identifies whether the cause is the filler itself, an upstream feed issue, or a recurring pattern on a specific crew.
Shift Consistency
Every supervisor gets the same AI-generated explanation and recommended actions for a given loss pattern. Reduces the knowledge gap between your most experienced team members and your newest.
Knowledge Base & Document Retrieval
AMI connects to your production documents, historical actions, and Navigator data. Teams retrieve the relevant SOP or the last time this fault was investigated, without searching across multiple systems.
AMI in action
AI Assistant analyses live line status and identifies the primary constraint with evidence
Automated Intelligence
Beyond answering questions, AMI proactively monitors production data and surfaces the things teams might miss.
Chronic Loss Detection
AMI identifies losses that recur over days or weeks, slowly enough to miss in a single-shift view, but significant enough to matter over a month. Surfaces them before they become structural performance issues.
MTBF-Based Next Best Actions
Monitors Mean Time Between Failures for every machine. When MTBF on a critical piece of equipment starts declining, AMI generates a proactive maintenance recommendation before the next breakdown.
Data Integrity & Signal Validation
AMI monitors PLC signals for anomalies, missing data periods, suspect event patterns, invalid SKU codes, and configuration mismatches. Flags data quality issues before they corrupt reporting.
Focused Improvement Prioritisation
Analyses loss patterns across sites and time periods to identify the chronic issues most worth targeting. Builds the evidence base for focused improvement projects automatically, so the decision on where to focus next is data-driven rather than instinct-driven.
AI Agents
AMI includes a growing set of specialist AI agents, each focused on a specific analytical task.
Machine Analysis Agent
Pull full fault history, MTBF trends, causal loss frequency, and recommended maintenance actions for any machine — in one command.
Site Analysis Agent
Generate a structured performance summary for any site: top loss drivers, crew comparison, trend analysis, and the three most impactful improvement opportunities.
Focused Improvement Agent
Connects historical performance data, previous actions, and industry benchmarks to structure a focused improvement project from problem identification through to outcome tracking.
