From Dashboards to Dialogue: How Rishi Transforms Manufacturing Intelligence

Challenges in Smart Manufacturing

Smart manufacturing promises productivity, efficiency, and flexibility through connected machines, industrial IoT, and advanced automation. But the reality on the shop floor is far more complex:

• Data Silos & Integration Issues: Incompatible machines and isolated systems limit access to critical information.
• Lack of Real-Time Visibility: Shop-floor insights are difficult to obtain, slowing decision-making when it matters most.
• Inconsistent Data Quality: Incomplete or unreliable sensor data diminishes the value of analytics.
• Manual Processes: Time-consuming, error-prone data collection hinders operational efficiency.
• The Talent Gap: Few workers possess both manufacturing domain knowledge and technical analytics expertise.

An Operator's Morning

Sunil, an experienced machine operator at a large automotive component plant, starts his shift facing a wall of dashboards from different vendors. Each shows partial, often conflicting information.

When a critical sensor warning flashes "low pressure on Line 2", he has no easy way to verify the data or determine if this is a real issue or just another sensor glitch. Sunil spends over an hour cross-referencing spreadsheets, digging through emails, and calling maintenance before the real problem is identified.

A Plant Manager's Dilemma

Priya, the plant manager, fights a daily battle against firefighting. She wants actionable insights on what's happening in her lines, but this requires writing SQL-like queries and deciphering cryptic charts—making collaboration with frontline staff cumbersome.

Quarterly reviews always end with the same frustration: "We have the data, but never the answers."

Engineers and the Talent Gap

Rohan, a new process engineer, is technically savvy but not yet versed in line operations. He gets lost in the maze of drilldowns and misses a subtle spike that later causes a production error—resulting in hours of unplanned downtime.

Rishi is Linecraft AI's industrial AI platform designed to deliver deep insights, natural language interactions, and advanced predictive capabilities to optimize plant operations and maintenance.

Scenario 1: Real-Time Issue Resolution

A packaging line unexpectedly slows down. In the old world, this would trigger hours of investigation.

With Rishi: The platform automatically detects the anomaly, highlights an unexpected trending increase in dwell time of a robotic arm, and summarizes the likely cause in plain language for Sunil:

Sunil acknowledges the alert and tasks maintenance—no guesswork, no downtime.

Scenario 2: Proactive Maintenance and Upskilling

Priya asks Rishi in plain language: "What are my top three lines at risk for unexpected downtime this week?"

Rishi instantly surfaces prioritized lines with confidence scores and explains:

Priya easily exports these insights for her maintenance meeting, building trust between data and action.

Scenario 3: Collaborative Continuous Improvement

Rohan, the process engineer, wonders why yields are dipping during night shifts. He simply asks: "Show me all process deviations by shift."

Rishi presents a clear, role-specific summary and suggests a root cause—part type changeover calibration skipped at midnight—saving both engineering and operational teams hours of troubleshooting.

Phase 1: Natural Language Manufacturing Intelligence

The foundation phase brings the power of conversational AI to manufacturing data:

• Natural Language Queries: Ask questions about your line in plain English—"What was my day-wise availability for the past week for machine A?"
• Contextual Responses: Get answers in text, graphs, or tables that are specific to your operations
• KPI Analysis: Deep dive into OEE, JPH, downtime, cycle time, MTBF, and more
• Trend & Root Cause Analysis: Understand why performance is changing and identify the underlying causes
• Statistical Operations: Perform correlation analysis, outlier detection, A/B testing, and more—all through natural language
• Report Generation: Create exportable HTML, PDF, Excel, or CSV reports instantly
• Role-Based Preset Questions: Get started quickly with curated questions for your role

Phase 2: Proactive Intelligence & Recommendations

Rishi evolves from answering questions to actively guiding operations:

• Enhanced Personalization: Suggested follow-up questions and persona-based interactions
• Auto-Inform Deviations: Proactive notifications when machines deviate from baseline parameters
• Process Drift Detection: Identify drifting processes and recommend parameter tuning
• Anomaly Detection: Spot anomalies with detailed insights and suggested fixes
• Machine Insights: Holistic comparative analysis of machine performance
• Benchmarking: Historical KPI comparisons with improvement margins
• Enterprise-Level Analysis: Multi-line, multi-plant intelligence
• Persistent Memory: Multiple chat instances with long-term conversation context

Phase 3: Predictive & Multi-Modal Intelligence

The ultimate evolution—anticipate the future and interact in any modality:

• Predictive Insights: Forecast machine states and maintenance needs to prevent unplanned downtime
• Anomaly Prediction: Identify potential anomalies before they impact production
• Drift Prediction: Predict process drifts and provide preventive solutions
• External Data Integration: Seamlessly connect with external structured manufacturing data sources
• RAG Framework: Continuous self-learning with the ability for users to provide additional context
• Custom Tools: Create and configure tools for custom metrics and unique use cases
• Multi-Modal Interaction: Communicate with Rishi via text, voice, media, and video—transforming how operators interact with intelligence on the shop floor