Intelligence at the Core. Not Bolted On.
Most platforms add AI as an afterthought — a chatbot here, a dashboard there. IRIS was architected with AI from day one. Every module has its own AI component. Every decision is informed by machine learning, LLM reasoning, and real-time recommendations.
WHAT AI-NATIVE MEANS
AI Is Not a Feature. It's the Foundation.
In IRIS, AI isn't a separate module you can turn off. It's woven into the architecture itself — the way data flows, decisions are made, and actions are executed.
Machine Learning
Predictive Intelligence
Classical and deep learning models trained on your operational data — and synthetic data from the Digital Twin when historical data is scarce. Time-series forecasting, classification, regression, and anomaly detection running continuously across every module.
LLM & RAG
Contextual Reasoning
Powered by LLMind — DBR77's proprietary industrial LLM — combined with Retrieval-Augmented Generation on your operational knowledge bases. Ask questions in natural language, get answers grounded in your SOPs, maintenance logs, quality records, and production data.
Recommendation Engine
Prescriptive Action
Cross-module analysis that doesn't just tell you what happened — it tells you what to do next. AI-generated recommendations become tasks with human approval gates, closing the loop from insight to execution automatically.
CLOSED-LOOP AI
From Data to Decision to Execution — Automatically.
This is what separates AI-native from AI-added. In IRIS, the loop never breaks: data generates insight, insight becomes a recommendation, the recommendation becomes a task, the task gets executed, and the outcome feeds back as new data.
Data
IoT sensors, production events, quality checks
Digital Twin
Simulation, scenario modeling, synthetic data
ML Model
Prediction, classification, anomaly detection
LLM Reasoning
Context analysis, root cause, explanation
Recommendation
Actionable insight with estimated impact
Task
Auto-created, assigned, prioritized
Human Approval
Review, approve, or reject
Execution
Work order, schedule change, parameter adjustment
Feedback
Outcome data feeds back into the loop
The loop is continuous. Every execution generates new data that improves the next prediction.
LLMIND
Meet LLMind. Our Proprietary Industrial LLM.
General-purpose LLMs don't understand your factory. LLMind does. Built by DBR77 specifically for manufacturing operations, it reasons about production processes, maintenance procedures, and quality standards the way an experienced plant manager would.
Purpose-Built for Manufacturing
LLMind is not a general-purpose chatbot fine-tuned for industry. It was trained from the ground up on manufacturing processes, operational terminology, and industrial workflows. It understands OEE, MTBF, changeover optimization, and batch scheduling natively.
On-Premise Deployment
For organizations with strict data sovereignty requirements, LLMind can be deployed entirely on-premise. Your operational data never leaves your network. No cloud dependency, no third-party data processing — full control.
RAG on Your Operational Data
Vector embeddings are generated from your SOPs, maintenance logs, quality records, production data, and knowledge bases. When LLMind answers a question, it retrieves relevant context from your data first — grounding every response in facts, not hallucinations.
Every Module Has Its Own AI
LLMind isn't a single chatbot sitting on top. Each IRIS module — MES, WMS, QMS, CMMS, APS — has its own AI component powered by LLMind, trained on module-specific data and optimized for module-specific tasks.
DIGITAL TWIN + AI
Train Models Without Waiting for Historical Data.
The biggest barrier to industrial AI? Not enough data. The Digital Twin solves this by generating synthetic training data from simulated scenarios — so your ML models are production-ready from day one.
Synthetic Training Data
Don't have 2 years of failure data to train a predictive maintenance model? The Digital Twin simulates thousands of scenarios — equipment degradation, process variations, demand spikes — generating the training data your ML models need, today.
Scenario Simulation
What happens if you add a third shift? Change the batch size? Move a workstation? Run the scenario in the Digital Twin before spending a dollar. Validate ROI with data, not gut feeling.
Continuous Model Validation
ML models degrade over time as conditions change. The Digital Twin continuously generates test scenarios to validate model accuracy, triggering retraining before predictions become unreliable.
USE CASES
AI That Solves Real Problems on the Shop Floor.
Not theoretical. Not future-state. These are production use cases running in IRIS today.
Predictive Maintenance
Vibration patterns, temperature trends, and cycle counts feed ML models that predict bearing failures 2–3 weeks before they happen. CMMS automatically schedules the work order. WMS reserves the spare parts. Zero unplanned downtime.
Quality Prediction
Process parameters from the current batch are compared against historical quality outcomes in real time. If the model detects drift toward a defect, operators receive an alert with specific parameter adjustments — before the defect occurs.
Demand Forecasting
Time-series models analyze order history, seasonality, and external signals to forecast demand 4–12 weeks ahead. APS uses the forecast to optimize production scheduling. MRP adjusts material procurement automatically.
Anomaly Detection
Unsupervised models continuously monitor hundreds of process variables across every production line. When something deviates from normal — even subtly — the system flags it, explains the likely cause via LLM reasoning, and recommends corrective action.
OEE Optimization
AI analyzes availability, performance, and quality losses across all lines, identifies the highest-impact improvement opportunities, and generates prioritized action plans. Digital Twin simulates each improvement before implementation.
Process Parameter Optimization
For every product-machine combination, ML models learn the optimal process parameters that maximize quality and throughput. Recommendations are pushed to operators in real time, with the reasoning explained by LLMind.
CONVERSATIONAL AI
Talk to Your Factory.
Ask questions in natural language. Get answers grounded in your operational data — not generic responses from a chatbot that doesn't know your plant.
Why did OEE drop on Line 3 yesterday?
OEE dropped 12% due to 3 unplanned stoppages. Root cause: bearing wear on conveyor C2 — CMMS flagged predictive maintenance 5 days ago but the work order wasn't prioritized. Recommend scheduling maintenance before next shift. Estimated recovery: +14% OEE.
What's the optimal batch size for Product X?
Based on changeover costs, demand patterns, and quality yield data, batches of 500 units minimize total cost per unit. Digital Twin simulation confirms 8% throughput improvement vs. current 200-unit batches, with no impact on quality metrics.
Which machines are most likely to fail this week?
3 machines flagged: Press #7 (bearing — 87% failure probability within 5 days), CNC-12 (spindle — 72% within 7 days), Conveyor B4 (motor — 65% within 10 days). Work orders have been drafted in CMMS. Approve to schedule?
DATA SOVEREIGNTY
Your Data. Your Infrastructure. Your Rules.
We know manufacturing data is sensitive. That's why IRIS gives you full control over where your data lives and how AI processes it.
On-Premise LLM
Deploy LLMind entirely within your own infrastructure. No data leaves your network. No cloud API calls. Full air-gapped operation for the most sensitive environments.
Tenant-Isolated AI
In multi-tenant SaaS mode, every AI model, every knowledge base, and every vector embedding is strictly isolated per tenant. Your data never trains another customer's model.
GOVERNANCE
Intelligence Guided by Human Judgment.
AI recommends. Leaders decide. Every AI-generated recommendation can be reviewed, approved, modified, or rejected before execution. Full audit trail on every decision. Because in manufacturing, accountability matters.
Approval Gates
Configurable per module, per role, per risk level
Full Audit Trail
Every recommendation, decision, and outcome is logged
Explainable AI
LLMind explains its reasoning in plain language
See AI-Native Manufacturing in Action.
Book a demo and see how IRIS turns your factory data into decisions — automatically.