High
Too much data still requires manual interpretation
Impact severity
Initializing Systems...
Autonomous Shop Floor Intelligence
Industrial AI Agents for Real-Time Action
Deploy specialized, autonomous AI agents across your production lines. From predictive maintenance to real-time quality root-cause analysis, our agentic framework turns reactive operations into proactive intelligence.
40%↓Unplanned Downtime
3xFaster RCA
24/7Autonomous
AGENT NETWORK4 Domains Active
LIVE
MaintIQPredicting failures
94%confidence
QualIQAnalyzing defects
87%confidence
ProdIQOptimizing throughput
91%confidence
ProcIQManaging materials
89%confidence
24/7
Monitoring
4
Domains
92%
Accuracy
Maintenance, quality, production, and procurement
Domain-specific industrial reasoning
Action-oriented outputs, not passive dashboards
Built on the data and execution foundation
Client Problems
Why industrial teams need agents instead of more passive reporting.
This solution page is about the operating problem AI agents are meant to solve.
High
Operational decisions conflict across functions
Impact severity
High
Investigations start after losses are visible
Impact severity
High
Generic AI is not operational enough
Impact severity
Operational Shift
From passive analytics to active industrial intelligence.
This comparison makes the solution intent immediately understandable.
Current stateAnalytics-only environment
Future stateAI-agent operating model
AI-agent operating model
- Agents continuously interpret plant conditions by domain.
- Recommendations are surfaced in context with intended action.
- Operational knowledge becomes more repeatable and scalable.
- Cross-functional reasoning becomes easier to operationalize.
Analytics-only environment
- People watch dashboards and decide manually.
- Root-cause work is reactive and slow.
- Knowledge remains trapped in experts and local teams.
- Cross-functional tradeoffs are difficult to surface in time.
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50% →
Future state
AI-agent operating model
- Agents continuously interpret plant conditions by domain.
- Recommendations are surfaced in context with intended action.
- Operational knowledge becomes more repeatable and scalable.
- Cross-functional reasoning becomes easier to operationalize.
How LeanQubit Solves It
A domain-led agent solution for industrial operations.
The solution is structured around how plants actually make and execute decisions.
Solution 01 of 04
Deploy specialized agents by problem domain
Different agents focus on maintenance, quality, production, or procurement rather than pretending one model should do everything equally well.
Proven Outcome
Each workflow gets more relevant industrial reasoning.
Solution Coverage
How the agent model spans industrial workflows.
This chart shows buyers that the solution is organized around operational domains, not a vague AI promise.
Maintenance responseHighest fit
failure risk, intervention timing, work order support
Quality intelligenceHigh fit
defect causes, containment, contextual investigation
Production guidanceHigh fit
bottlenecks, line conditions, throughput decision support
Material decision supportModerate fit
consumption trends and replenishment coordination
Illustrative span of agent coverage in a typical plant.
Solution Flow
How AI agents fit into plant decision loops.
A compact step flow keeps the solution understandable on mobile and desktop.
01
StepObserve data and execution context
Agents consume plant signals and history from the connected industrial stack.
02
StepInterpret the operational condition
Each agent reasons within its domain using the appropriate industrial context.
03
StepRecommend action or escalation
The platform surfaces the intervention or next step that matters most.
04
StepClose the loop through workflows
Teams act, outcomes are recorded, and future recommendations get stronger.
Reference Architecture
How the AI-agent solution fits into LeanQubit.
This connects the agent concept back to the rest of the operational stack.
Connected Modules
What the agent solution depends on.
This makes the broader solution stack visible to buyers.
Module 01
FactoIQ
Hosts the industrial intelligence and agent orchestration layer.
Part of LeanQubit Platform
Module 02
FactoLake
Provides the contextual history and operational data foundation the agents reason over.
Part of LeanQubit Platform
Module 03
FactoMES
Connects recommendations to execution workflows and operator-facing action.
Part of LeanQubit Platform
Module 04
IIoT Integration
Supplies the live machine and process data that keeps agent decisions grounded in plant reality.
Part of LeanQubit Platform
Buyer Value
What the AI-agent solution changes for the plant.
This grounds the solution in role-specific value.
Value for Operations Leader
"A shorter path from plant conditions to recommended operational response."Talk to us
Key metrics
- OEE upliftavg across plants0%
- MTTR reductionfaster fault resolution0%
- Shift visibilityreal-time coverage0%
Implementation Model
How to introduce the agent solution in phases.
This keeps the adoption model practical.
Start your journey Typical deployment: 6–8 weeks
FAQ
Questions buyers ask about AI agents in manufacturing.
Showing 3 of 3 questions
Next Step
Put AI where it changes industrial action, not just reporting.
Book an AI readiness assessment to identify the first operational workflow where agents can create value and how LeanQubit can stage that rollout responsibly.