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.
OT Layer
PLCs · Sensors · MES · SCADA
IT Layer
ERP / SAP · Data Warehouse
Mentis OS
Data Integration · Intelligence · Explainability
Decision Makers
Operators · Engineers · Leadership
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
Quality Reports & NCRs
Free-text, images
Maintenance Logs
CMMS entries
Engineering Guides
OEM specs
Shift Handover Notes
Operator observations
Parses PDFs, scanned docs, images. Extracts procedures, parameters, thresholds.
Links sensor anomalies to maintenance history, quality observations to process parameters.
Insights grounded in both real-time telemetry and institutional knowledge.
Every insight traces back to its source document, sensor, or log entry.
Sensor & IoT Streams
Vibration, temp, pressure
Historians & Time Series
OSIsoft PI, InfluxDB
MES & SCADA
Production orders
ERP / SAP
Orders, inventory
Quality Management
SPC, 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"
— QC, 3 days ago
Root Cause — 87% Confidence
Main bearing degradation. 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
Live sensor data feeds directly into the analysis pipeline
Maintenance logs, NCRs, and OEM manuals 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 linked 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.
AFFECTED
5 lots · 240 parts
ACTION
Quarantine initiated
SPC monitoring meets AI. Detects out-of-control conditions, correlates to process parameters, and recommends containment.
Trend and rule violation detection
Western Electric rules applied automatically
Root cause across data types
Links SPC drift to tool wear, maint history
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.
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
Scanning sensor data, maintenance records, quality logs, and engineering documentation.
06:00
Shift start — normal ops
06:47
Conveyor misalignment
42 min
08:12
Vibration spike V-401
4.2 mm/s
10:33
Quality exceedance
Bore OOC
11:32
OEE drops to 78.4%
Vibration V-401 trending since Nov 3 — bearing wear signature
"Slight vibration noted during PM" — J. Torres, 12 days ago. No WO created.
§7.3: Replace bearing at >3.5 mm/s for >24h.
Batch 2847 supplier: density variance +0.3% vs. spec.
Primary: Cascading mechanical degradation
Bearing wear on Press 4 caused progressive vibration, inducing conveyor tensioner stress leading to misalignment at 06:47.
Contributing: Material variance
Batch 2847 density variance (+0.3%) narrowed tolerance margins, pushing bore diameter out of control.
Process Gap Identified
Maintenance observation from Nov 3 was logged but no corrective WO generated. SOP-M-142 §3.2 requires WO creation for any vibration trend.
Replace Press 4 main bearing
Failure within 48-72h. Cost: $3.2K vs. $47K unplanned.
Replace Station 4 milling cutter
At 2,847/3,000 cycles. Schedule with bearing PM.
Close WO generation gap in SOP-M-142
Recommend mandatory WO trigger for vibration trends.
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, 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."