For Complex Industrial Systems
Manufacturing organizations generate vast volumes of data — from machines, sensors, production systems, and enterprise platforms. Yet most operational decisions are still driven by manual analysis, delayed reports, and fragmented insights.
Genovation converts operational data into decision-ready intelligence — without disrupting existing systems.
Intelligence must earn the trust of the floor before it reaches the boardroom.
Plants generate data continuously, but insights arrive too late, without root causes, or disconnected from SOPs.
Dashboards show what happened. They rarely explain why.
Production decisions affect safety, quality, throughput, and cost. Unexplainable AI recommendations are rarely accepted.
The floor needs to trust the insight before acting on it.
Legacy systems, OT-IT separation, and on-premise requirements are the norm. Cloud-centric AI often fails to fit.
Many AI solutions can't meet manufacturing realities.
Manufacturing knowledge lives everywhere — in sensor streams, PDF manuals, maintenance logs, quality reports, and tribal expertise. Genovation consolidates structured and unstructured data into a unified intelligence layer without forced rip-and-replace.
SOPs & Work Instructions
1,247 documents · PDF, Word
Quality Reports & NCRs
Free-text observations, images
Maintenance Logs
Technician notes, CMMS entries
Engineering Guides & Manuals
OEM specs, troubleshooting trees
Shift Handover Notes
Operator observations, exceptions
Parses PDFs, scanned docs, images. Extracts procedures, parameters, thresholds, and cross-references.
Links sensor anomalies to maintenance history, quality observations to process parameters, failures to OEM specs.
Generates insights grounded in both real-time telemetry and institutional knowledge — not just pattern matching.
Every insight traces back to its source document, sensor, log entry, or database record.
Sensor & IoT Streams
Vibration, temp, pressure, flow
Historians & Time Series
OSIsoft PI, Aveva, InfluxDB
MES & SCADA
Production orders, batch data
ERP / SAP
Orders, inventory, scheduling
Quality Management (QMS)
SPC data, inspection records
Ingests PDFs, scanned documents, spreadsheets, operator notes, and engineering manuals. No manual tagging required.
Correlates a vibration spike with a maintenance log from last week, a quality NCR, and the OEM bearing spec — automatically.
On-premise, private cloud, or edge-adjacent. No data leaves your network. Respects OT-IT boundaries.
"Why is Press Line 4 vibrating above threshold and what should we do about it?"
Threshold 3.5 exceeded
MAINT-LOG-4847
"Slight vibration noted during PM check, bearing within spec but trending"
— J. Torres, 12 days ago
OEM-MANUAL-P4 §7.3
"Replace main bearing when vibration exceeds 3.5 mm/s for >24h"
NCR-2024-0892
"Dimensional drift on parts from Press 4 — tolerance exceedance on bore diameter"
— QC, 3 days ago
Root Cause — 87% Confidence
Main bearing degradation. Pattern matches Failure-DB-2847. Maint log from 12 days ago confirms early detection. OEM manual §7.3 specifies replacement at current threshold.
Connected Evidence
Schedule preventive maintenance within 24 hours
WINDOW
Sun 06:00-10:00
Live sensor data feeds directly into the analysis pipeline
Maintenance logs, NCRs, and OEM manuals are retrieved and reasoned over
AI correlates all sources into a single explainable root cause
Actionable recommendation with cost impact and audit trail
87.3%
OEE
92.1%
Availability
94.8%
Performance
98.2%
Quality
Root cause analysis identified 3 contributing factors:
Recommended Actions
Schedule conveyor inspection per SOP-M-142. Review material supplier variance from Batch 2840-2850.
Not just metrics — explanations. Every KPI comes with an AI-generated root cause that links to specific process events, sensor readings, and SOPs.
Auto-generated OEE breakdown
By availability, performance, and quality
Root cause tied to process data
Sensor logs, CMMS records, quality events
Recommendations linked to SOPs
Specific procedures and document references
Ask questions in natural language. Get answers grounded in your actual SOPs, work instructions, and safety procedures — with source citations.
Natural language SOP access
No more searching through binders and PDFs
Safety notes auto-surfaced
PPE requirements and hazard warnings inline
Always current revision
Outdated or conflicting procedures flagged
What's the procedure for clearing a jam on the packaging line?
Per SOP-PKG-023 (Packaging Line Jam Clearance):
⚠ Safety Note: PPE required — safety glasses, cut-resistant gloves (Class 5)
Tool wear on Station 4 cutter (2,847 cycles vs. 3,000-cycle spec from OEM-MANUAL §5.1). Temperature compensation masking drift. Quality NCR-2024-0889 confirms dimensional impact.
AFFECTED
5 lots · 240 parts
ACTION
Quarantine initiated
SPC monitoring meets AI. Detects out-of-control conditions, correlates to process parameters, and recommends containment — pulling from quality reports, sensor data, and engineering specs.
Trend and rule violation detection
Western Electric rules applied automatically
Root cause across data types
Links SPC drift to tool wear, maint history, and OEM specs
Batch genealogy and containment
Affected lots identified and quarantined
Ask questions in plain language across all plants. Get answers backed by source records — not summaries of summaries.
Multi-turn conversational Q&A
Follow-up questions refine the analysis
Cross-plant comparison
Performance contextualized across sites
Cost impact quantification
Maintenance backlog vs. failure cost analysis
Why did Plant 2 miss target last week?
Plant 2 missed production target by 4.2% last week:
Plant 1
+1.3%
Plant 2
-4.2%
Plant 3
+0.8%
PM schedule review recommended — last service 23 days overdue per SOP-M-142.
What would it cost to bring Plant 2 PM schedule up to date?
Estimated backlog cost: $18,400 (12 overdue WOs). Projected downtime savings next quarter: $127,000 based on historical failure rates.
When an anomaly is detected, Genovation reconstructs the entire time window — surfacing concurrent failures, correlating across data sources, and synthesizing actionable insights.
Anomaly Trigger: OEE dropped to 78.4% — 12 points below target
Investigating 6-hour window around event. Scanning sensor data, maintenance records, quality logs, and engineering documentation.
06:00
Shift start — normal ops
06:47
Conveyor misalignment
Station 2 · 42 min downtime
08:12
Vibration spike V-401
Press 4 · 4.2 mm/s
09:15
Feed rate deviation
Batch 2847 · density +0.3%
10:33
Quality exceedance
Bore diameter OOC · 18 parts
11:32
OEE drops to 78.4%
Investigation triggered
Vibration V-401 trending since Nov 3 — bearing wear signature matches Failure-DB-2847
"Slight vibration noted during PM" — J. Torres, 12 days ago. No work order created.
§7.3: Replace bearing at >3.5 mm/s for >24h. §5.1: Cutter life 3,000 cycles (current: 2,847)
Dimensional drift on Press 4 parts — bore diameter tolerance exceedance, filed 3 days ago
Batch 2847 supplier: density variance +0.3% vs. spec. Same supplier flagged in QMS for Lot 440
Primary: Cascading mechanical degradation
Bearing wear on Press 4 (detected but not actioned 12 days ago) caused progressive vibration increase, which induced conveyor tensioner stress on Station 2, leading to misalignment at 06:47.
Contributing: Material variance amplified quality impact
Batch 2847 density variance (+0.3%) narrowed tolerance margins. Combined with tool wear at 2,847/3,000 cycles, this pushed bore diameter measurements out of control.
Process Gap Identified
Maintenance observation from Nov 3 was logged but no corrective work order was generated. SOP-M-142 §3.2 requires WO creation for any noted vibration trend.
Replace Press 4 main bearing
Failure within 48-72h. Window: Sun 06:00-10:00. Cost: $3.2K vs. $47K unplanned.
Replace Station 4 milling cutter
At 2,847/3,000 cycles. Schedule with bearing PM to minimize downtime.
Flag Batch 2847 supplier for incoming QC
Per QMS-IR: density variance on this and Lot 440.
Close WO generation gap in SOP-M-142
Observation on Nov 3 was logged without action. Recommend mandatory WO trigger for vibration trends.
Avoided failure + reduced scrap + supplier correction
Reconstructs the full timeline around an anomaly to find concurrent events
Correlates sensor data, maint logs, quality NCRs, OEM specs, and ERP records
Synthesizes root cause across failures — not just one event, the full causal chain
Prioritized actions: immediate fixes, short-term corrections, and systemic improvements
Explanation, not just visualization
Insights that answer "why", not just "what"
Designed for OT-aware environments
Respects operational technology boundaries
Respects infrastructure investments
Works with existing systems, no forced replacement
Builds trust across roles
From operators to engineers to leadership
Adopted where credibility matters more than novelty.
Plant Managers
Operations Leadership
Industrial Engineering Teams
Supply Chain & Procurement Heads
CIOs & Digital Transformation Leaders
Manufacturing engagements typically begin with:
Defined Operational Use Case
Clear scope and measurable objectives
Controlled Pilot Deployment
Validated on selected lines or processes
Integration with Selected Data Sources
Historians, databases, document systems
Phased Expansion
Across plants or functions based on results
All deployments prioritize continuity of operations and system reliability.
If your organization is exploring AI adoption across industrial operations and requires explainable, deployable intelligence, we welcome a conversation.
"In manufacturing, intelligence must earn the trust of the floor before it reaches the boardroom. Genovation is built for that reality."