Unplanned downtime costs manufacturers $50 billion annually, and average OEE sits at just 60%. Lumina connects your MES, ERP, and historian data into a single conversational layer so operators, engineers, and planners can act on insight instead of searching for it.
No sign-up required. Your data stays in your environment. Local-first architecture.
The Strategic Gap
McKinsey reports that manufacturers using AI for quality have reduced defect rates by 30-50%. Yet 86% of manufacturers say smart factory solutions will be the main driver of competitiveness by 2025 (Deloitte). The gap between data collection and data-driven action remains the single largest barrier to operational excellence.
Process data lives in OSIsoft PI, SCADA, and MES systems that were never designed to surface cross-functional insight. Engineers spend hours pulling manual reports instead of solving problems.
With unplanned downtime costing manufacturers $50B annually (Aberdeen), most plants still run to failure. Condition monitoring data exists but rarely reaches the people who schedule work orders.
Average first pass yield losses cost 10-15% of revenue. SPC charts are reviewed after the fact, and root cause analysis relies on tribal knowledge rather than systematic data correlation.
Demand forecasts, inventory levels, and supplier scorecards live in disconnected spreadsheets. SKU-level accuracy suffers, leading to excess inventory or stockouts that erode margins.
Average OEE in manufacturing is 60%, meaning 40% of productive capacity is lost. Dashboards show the number, but they do not explain why availability, performance, or quality dropped on a specific shift.
Experienced operators and maintenance technicians are retiring faster than knowledge can be transferred. Critical troubleshooting expertise walks out the door with every departure.
What Lumina Does
Lumina does not replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Lumina's Exploration engine continuously runs hypothesis tests: anomaly clustering, correlation analysis, severity scoring. It delivers findings proactively, the way an experienced operator spots patterns that 'don't look right'.
Through the Dialogue Intelligence Framework™, a six-layer cognitive architecture, agents reason across all your data sources simultaneously. Fragmented data becomes connected understanding.
Configured by your engineers, trained on your procedures, compounding context over time. Each interaction builds a knowledge base that scales.
Tensions that take days in meetings are resolved in seconds. Agents vote, alert, and challenge each other, with every position grounded in data and rationale visible.
Every query is valid SQL. Every calculation executed by a database, not an LLM. When someone asks 'how did you calculate that?', the agent shows the query, the data, and the method.
Your data does not move to us. SaaS, private cloud, on-premises, or air-gapped. Bring your own LLM through Ollama, or export agents for Claude, Copilot, or any MCP-compatible tool.
How It Works
The Dialogue Intelligence Framework™ transforms how manufacturing teams interact with operational data. Instead of navigating dashboards or waiting for analyst reports, plant managers, process engineers, and maintenance leads ask questions in natural language and receive contextual, citation-backed answers drawn from your MES, ERP, historian, and CMMS systems.
Ask questions in plain language. Agents respond with reasoned answers, suggest follow-up questions, and guide you toward the decision you need to make.
Proactive scan engine for your data: cluster analysis, correlation, Benford's law, severity scoring. Proactive hypothesis testing that surfaces what you did not know to look for.
Raw findings are synthesized into composites that connect cause, context, and consequence. Not just 'this is anomalous' but 'here is what it means for your operation'.
A six-engine pipeline (DSP, Policy, Semantic Guard, LLM, Classifier, Memory, Audit) judges what agents can access, say, and do. Per-agent configuration. Zero trust by default.
Agents build context over time through a three-tier memory architecture: Local knowledge, team-level awareness, and organizational intelligence that compounds with use.
Reasoning and tools are separated by design. LLMs plan the query. DuckDB executes the calculation on your data. No black-box results. Every step visible.
This is not a chatbot with a database connection. It is a complete cognitive architecture where every layer serves a purpose and every output is traceable.
Explore the Full DIF ArchitectureUse Cases by Domain
OEE, SPC, Root Cause Analysis, First Pass Yield
Manufacturers using AI-driven quality systems have reduced defect rates by 30-50% (McKinsey, 2023). Production intelligence connects line performance, statistical process control, and yield optimization into a single conversational layer so engineers can move from detection to correction in minutes, not days.
Decompose OEE into availability, performance, and quality factors across lines and shifts. Identify bottlenecks, track micro-stops, and correlate downtime codes to root causes through natural language queries.
Monitor Cp/Cpk indices, control chart violations, and out-of-spec trends conversationally. Ask which parameters are drifting, predict specification breaches, and trace variation back to specific machines or material lots.
Perform Pareto analysis on defect types, correlate fishbone diagram variables with quality outcomes, and compare shift-level variance. Surface patterns that manual investigation would take weeks to uncover.
Track rework costs, scrap rates, and process parameter correlations to improve first pass yield. Identify which recipe settings, raw material batches, or environmental conditions drive the highest rejection rates.
McKinsey & Company, "AI-driven quality improvements in manufacturing," 2023.
Predictive Maintenance, Spare Parts, Turnaround Planning
Unplanned downtime costs manufacturers an estimated $50 billion annually (Aberdeen Group). Moving from reactive to predictive maintenance requires more than sensors. It requires connecting vibration data, thermal trends, and work order history into a system that maintenance planners can actually query without writing SQL or building custom reports.
Track vibration trending, thermal analysis, and bearing fault signatures conversationally. Monitor MTBF and MTTR by asset class, predict failure windows, and prioritize work orders based on criticality and production schedules.
Apply ABC analysis to spare parts inventory, predict lead times for critical components, and optimize safety stock levels. Reduce carrying costs while ensuring availability for high-criticality assets.
Model critical path schedules, perform resource leveling across crafts, and estimate turnaround costs with historical benchmarking. Compare planned versus actual durations from previous shutdowns to improve future accuracy.
Aberdeen Group, "The cost of unplanned downtime in manufacturing," 2022.
Demand Forecasting, Inventory Optimization, Supplier Performance
Supply chain disruptions since 2020 have exposed the fragility of spreadsheet-driven planning. Manufacturers need SKU-level demand visibility, dynamic reorder points, and real-time supplier scorecards. Conversational intelligence makes these capabilities accessible to planners without requiring data science expertise.
Generate SKU-level demand predictions, model seasonality patterns, and quantify promotional lift effects. Ask about forecast accuracy by product family, compare statistical methods, and adjust projections through dialogue.
Monitor days of supply across warehouses, calculate dynamic reorder points, and analyze carrying cost impact. Identify slow-moving inventory, flag potential stockouts, and simulate safety stock scenarios conversationally.
Score suppliers on delivery reliability, track incoming quality inspection trends, and analyze lead time variability. Compare vendor performance across categories and surface risks before they impact production schedules.
Across the Enterprise
Manufacturing decisions extend beyond the plant floor. Lumina supports corporate functions with the same conversational intelligence, connecting workforce, financial, compliance, and IT data into a unified dialogue layer.
Optimize shift schedules based on production demand, track skills matrix coverage, monitor overtime trends, and identify training gaps across operator certifications.
Analyze cost per unit by product line, decompose margin variance by plant, and track manufacturing overhead absorption. Connect financial outcomes to operational drivers conversationally.
Monitor ISO 9001, IATF 16949, and FDA compliance status across facilities. Track CAPA closure rates, audit findings, and corrective action effectiveness through natural language queries.
Monitor MES and ERP integration health, track data pipeline latency from historian to analytics, and surface system anomalies before they impact production reporting.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
See how Lumina connects your MES, ERP, and historian data into a conversational layer that helps operators, engineers, and planners make faster, better-informed decisions.
Learn how the DIF structures conversational AI around your manufacturing data. From historian queries to maintenance planning, the framework ensures every answer is grounded in your operational reality.
Deep DiveWhy local-first, sovereign AI architecture matters for industrial environments. Explore how manufacturers can maintain data control while deploying intelligence at the edge.
InsightDashboards visualize metrics, but they do not drive action. This insight explores why manufacturing teams need conversational intelligence that connects data to decisions on the plant floor.
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