For Robotics · Automation · Advanced Manufacturing

Predict Every Failure Before It Stops The Line.

VSI deploys production-grade agentic AI that ingests real-time telemetry from every sensor on every unit — building individual wear signatures, predicting failures 47 days before they occur, and triggering autonomous corrective action without stopping production.

Complete AI Intake →

Predictive Maintenance

Fleet Intelligence

OEE Optimisation

ROS2 Native

SCADA Integration

Digital Twin Sync

OPC-UA · MQTT · Modbus

30-Day Deployment

UNIT-217 · LIVE TELEMETRY

UPTIME98.7%

OEE SCORE91.3%

MTBF2,847h

J1 TEMP47.3°C ↑

VIBRATION0.12mm/s

RUL BEARING847 hrs

Live Fleet Intelligence

Real-Time Data.
Every Unit.
Every Second.

This is what VSI's fleet intelligence platform looks like in production — 340 units, live telemetry, failure predictions updating continuously, and zero manual intervention required.

                            Fleet Health Map — 340 Units

                            LIVE
                        

                            Nominal
                            312
                        

                            Warning
                            19
                        

                            Critical
                            4
                        

                            Maintenance
                            5
                        

                        Fleet OEE — 30d Avg

91.3%

                        ↑
                        +18.6pts since VSI deployment

Availability 98.2%

Performance 94.1%

Quality 93.8%

Vibration FFT — UNIT-217 ⚠ Anomaly 0.42mm/s

Sampling: 40kHz · Per-axis Stage 2 Bearing

                        Failure Predictions — 30d

UNIT-089 Bearing BPFI 7d

UNIT-217 Winding fault 14d

UNIT-301 Encoder drift 21d

UNIT-044 Gear wear stage-1 28d

UNIT-156 Thermal anomaly 43d

                        14 active predictions · Avg lead: 47d

                        MTBF Trend

2,847 hrs

                        ↑ 3.1×
                        pre-AI baseline

Baseline: 918 hrs Now: 2,847 hrs

Live Alert Feed 340 units

12:04UNIT-334 · Encoder drift compensated autonomously

12:02UNIT-089 · BPFI anomaly escalated → PM scheduled

11:58UNIT-156 · RUL updated: 847hrs remaining

11:54UNIT-078 · Post-PM FFT normalised · Cleared

11:51UNIT-217 · MCSA fault class 4 → work order created

                            6-Axis Industrial Arm

Predictive maintenance · 340-unit fleet

                            AMR Fleet Coordination

ROS2 integration · 78 AMRs · +34% throughput

                            Fleet Intelligence HQ

Real-time OEE · Live telemetry · 24/7 monitoring

                ◆ Live data simulated from production deployment◆ 340 units · 4
                    facilities◆ All
                    metrics production-verified
            

AI Systems for Robotics

Six Systems.
Zero Unplanned
Downtime.

Purpose-built agentic AI for robotic systems and autonomous machines. Every system integrates with your existing ROS2, SCADA, PLC, and MES infrastructure — no rip-and-replace, no production disruption, live in 30 days.

SYS-01 · Predictive Intelligence

🔬

Predictive Failure Intelligence

Ingests real-time telemetry from every sensor on every unit — vibration FFT, thermal, acoustic emission, MCSA, encoder position — building an individual wear signature per machine. Detects stage 1–2 degradation precursors an average of 47 days before failure threshold alarms would fire.

Vibration FFTMCSAAcoustic AEThermalRUL Prediction

SYS-02 · Fleet Coordination

🦾

Autonomous Fleet Intelligence

Multi-robot coordination AI evaluating 40+ variables per assignment in real time — unit health score, battery SoC, cycle time history, maintenance proximity, payload capacity, traffic density. Maximises throughput without dispatcher intervention. ROS2 native integration.

ROS2AMR/AGVPath Optimisation40+ VariablesLoad Balancing

SYS-03 · OEE Intelligence

📊

OEE Optimisation Engine

Continuous Availability × Performance × Quality monitoring with automated root cause analysis. Identifies micro-stoppages, speed losses, and quality deviations before they compound. Production manager receives ranked corrective actions — not raw SCADA data dumps.

OEE Real-TimeRoot Cause AIMicro-StoppageMES BridgeSPC/SQC

SYS-04 · Digital Twin

🔄

Digital Twin Synchronisation

Live digital twins updated continuously from production telemetry. Physics-based failure simulation runs ahead of real-world conditions — predicting failure modes under variable load profiles. FEA integration surfaces structural fatigue before it becomes observable degradation.

Physics SimFEA IntegrationLoad Profiling100ms SyncFailure Modes

SYS-05 · Maintenance Operations

⚙️

Condition-Based Maintenance AI

Replaces fixed-interval PM schedules with AI-optimised condition-based maintenance (CBM). Schedules maintenance during production gaps, coordinates parts inventory and technician availability, generates CMMS work orders automatically, and verifies post-PM performance before returning units to line.

CBMCMMS Auto-WOParts ForecastingPM VerificationMTBF Tracking

SYS-06 · Process Intelligence

🧠

Manufacturing Process AI

Correlates machine state, material properties, environmental variables, and quality outcomes to identify interaction effects invisible to conventional SPC. Closes the loop between quality gates and process parameters autonomously — reducing defect rates without engineering intervention between production runs.

SPC/SQCProcess ClosureDefect CorrelationERP BridgeQuality Gates

The Operational Reality

Every Hour of
Unplanned
Downtime
Has a Price Tag.

The average manufacturer loses $260,000 per hour of unplanned downtime. Automotive and semiconductor operations: multiples higher. The failures causing it are predictable. Your current systems just aren't predicting them.

                        FAULT-CLASS-01 · Bearing Degradation

Stage 1–2 Degradation Caught Too Late

Traditional monitoring detects bearing failures at stage 3–4 — hours before catastrophic failure. VSI's vibration FFT and acoustic emission AI detects stage 1–2 degradation weeks in advance. Average lead time: 47 days.

                        FAULT-CLASS-02 · Motor Insulation

Thermal Runaway & Winding Fault Detection

MCSA detects insulation degradation and winding faults months before motor failure. Average VSI detection lead: 47 days. Emergency motor replacement downtime: 4–7 days. The math on prevention is obvious.

                        FAULT-CLASS-03 · Encoder Drift

Positional Accuracy Loss — Invisible Until It Fails

Incremental encoder drift accumulates invisibly until positioning errors cause quality failures or collisions. VSI detects sub-millimetre drift trends and triggers autonomous recalibration before the first defective part is produced.

                        FAULT-CLASS-04 · Fleet Blind Spots

No Cross-Fleet Pattern Recognition

When UNIT-089 fails, you check UNIT-089. VSI checks all 340 units for the same signature and schedules every affected unit simultaneously. Fleet-wide pattern learning prevents the same failure from occurring twice.

Real-Time Telemetry Intelligence

Every Sensor.
Every Unit.
Every Second.

VSI AI ingests telemetry from every sensor on every unit in real time — building individual wear signatures, detecting anomalies, and predicting failure modes at the component level.

Vibration FFT

40kHz

Fast Fourier Transform spectral analysis detecting bearing defect frequencies, imbalance, misalignment, and resonance modes at 40kHz sampling.

Per-axis · 0.1Hz resolution · <100ms alert latency · Stage 1–2 detection

Thermal Imaging

±0.1°C

Continuous thermal profiling of motors, drives, and gearboxes. Detects insulation degradation and friction anomalies before threshold breach.

10Hz rate · 90-day rolling trend · 47-day avg prediction lead

MCSA

12 Classes

Motor Current Signature Analysis detects rotor faults, bearing defects, and eccentricity from current draw patterns — non-intrusive, no physical contact required.

0.5% sensitivity · 12 fault classes · Per-phase analysis

Positional Accuracy

±10μm

Encoder monitoring at 10kHz. Detects drift, backlash increase, and wear before positional errors cause quality failures or collisions. Auto-recalibration triggered at ±2μm.

10kHz monitoring · Auto-recal trigger · Quality gate sync

Acoustic Emission

1MHz

AE/UE monitoring detects micro-crack propagation, surface fatigue, and lubrication film breakdown at frequencies invisible to vibration analysis and human senses.

100kHz–1MHz · 0.1mm crack detection · Lube film: 3μm sensitivity

RUL Prediction

95% CI

Remaining Useful Life prediction per component per unit, continuously updated from live telemetry. LSTM + Transformer + Physics-informed models with confidence intervals.

LSTM · Transformer · Physics-informed · Per-component · 95% CI

Process Variables

100+

OPC-UA, Modbus, PROFINET, EtherNet/IP, MQTT, REST. All process variables — pressure, flow, temperature, humidity, particulate — correlated with machine health and quality outcomes.

OPC-UA · Modbus · PROFINET · EtherNet/IP · MQTT

Digital Twin Δ

100ms

Continuous comparison of digital twin simulation vs actual telemetry. Divergence triggers model update, anomaly flag, or maintenance recommendation. FEA-linked.

100ms sync · Configurable threshold · FEA integration · Auto-update

Documented Results

Production
Outcomes.
Not Simulations.

Real results from live VSI deployments across robotics and advanced manufacturing. Every number verifiable. Every client real. Updated quarterly.

Advanced Manufacturing · 4 Facilities · 340 Robotic Units

−81%

Unplanned downtime eliminated — 340-unit fleet in 14 months

A precision manufacturing group absorbing $2.3M annually in unplanned downtime. Not from catastrophic failures — from slow component degradation invisible to threshold-based monitoring. VSI deployed fleet-wide predictive intelligence ingesting vibration FFT, thermal, acoustic, and MCSA telemetry simultaneously. Individual wear signatures built per unit over the first 30 days. Failure precursors detected an average of 47 days before occurrence. Maintenance rescheduled from fixed-interval to condition-based. 81% downtime reduction in 14 months. 94 failures predicted and prevented. No line stoppage attributable to equipment failure since Q1 last year. OEE across all facilities improved 18 points.

Predictive Maintenance AI340 Robotic UnitsFFT · Thermal · MCSACBM Scheduling

Automotive Assembly · Multi-Line · 78 AMRs

+34%

AMR fleet throughput increase — zero new hardware

78 Autonomous Mobile Robots experiencing chronic throughput bottlenecks from inefficient task assignment and charging cycles causing 6–8 hour congestion events per shift. VSI fleet coordination AI integrated directly with ROS2 and WMS, evaluating 40+ variables per assignment in real time. Charging windows staggered across the fleet. Path planning re-optimised dynamically by production priority. Throughput up 34% in the first quarter. Same 78 robots now doing the work previously requiring 15 additional units. No hardware purchased. ROI achieved in month 3.

Fleet Coordination AI78 AMRsROS2 Integration+34% Throughput

Semiconductor Fab · Class 10 Cleanroom · 24 Six-Axis Arms

±2μm

Encoder drift eliminated — zero manual recalibration events in 11 months

Thermal expansion of arm structure interacting with encoder mounting tolerances created incremental drift requiring 6–8 hours of manual recalibration per cell every few weeks. VSI positional telemetry AI monitored encoder feedback at 10kHz, correlated drift with thermal data, and triggers automated micro-recalibration at ±2μm — autonomously, without taking the cell offline. Manual recalibration events: from 3–4 per month per unit to zero in 11 months. In a Class 10 cleanroom environment, recalibration-free operation is a significant production and contamination control win.

Positional Telemetry AISemiconductor Fab10kHz EncoderAuto-Recalibration

Consumer Electronics · High-Volume · 96 Collaborative Robots

−45%

Defect rate reduction through AI-driven process closure

4.7% defect rate with no identifiable root cause — failures appearing random across 16 cells, 96 cobots, multiple shifts and product variants. VSI process AI correlated quality outcomes with the full matrix of variables across SPC, MES, ERP, and robotic controllers. Three dominant interaction effects identified in 45 days: humidity above 58% × solder paste lot = 3× void rate; tool wear above 72% cycle count = 89% of cosmetic defects; specific shift changeover pattern = thermal equilibrium disruption. Corrective responses automated. Defect rate down 45% in 90 days. Engineers had been chasing this problem for two years.

Process AISPC · MES · ERP Integration96 Cobots−45% Defects

Operational Impact Data

Numbers Your
Plant Manager
Will Print Out.

Average outcomes from VSI robotics AI deployments. All figures from production, not simulation.

Metric	Pre-VSI Baseline	Post-VSI AI	Impact
Unplanned downtime rate	15–20% capacity lost	↓ 81% reduction	$2.3M/yr eliminated
MTBF (Mean Time Between Failures)	Unit historical baseline	↑ 3.1× improvement avg	Fewer failures, longer cycles
OEE (Availability × Performance × Quality)	Industry avg: 60–65%	↑ +18 points average	Equivalent to +1 shift/day
Failure detection lead time	Stage 3–4 (hours before)	↑ Stage 1–2 (47 days avg)	Planned replaces emergency
Maintenance cost per unit	Fixed-interval PM + emergency	↓ Condition-based only	35% maintenance cost reduction
AMR fleet throughput	Constrained by manual dispatch	↑ +34% avg	15 fewer units needed
Defect rate (process AI)	Industry avg: 3–5%	↓ 45% reduction	Quality cost eliminated
Year-one ROI	Implementation cost	3.8× average return	Positive within 90 days

◆ ◆ ◆ ◆ ◆

"We operate 340 robotic units across four facilities. Unplanned downtime was costing us $2.3M annually — not from catastrophic failures, but from degradation we couldn't detect until something stopped. VSI built a predictive intelligence system that ingests sensor data from every unit in real time, models failure probability using each machine's individual wear signature, and triggers maintenance automatically before failure occurs. In 14 months, unplanned downtime is down 81%. The system has predicted and prevented 94 failures. We haven't had a line stoppage attributable to equipment failure since Q1 last year."

VP of Manufacturing OperationsAdvanced Manufacturing Group · 4 Facilities · 340 Robotic Units

// AMR Fleet · Automotive Assembly //

"Our 78 AMRs were creating 6–8 hour congestion events every shift. VSI's fleet AI restructured charging windows, re-optimised path assignments in real time, and dynamically prioritised tasks by production urgency. Throughput up 34% in the first quarter. Same robots, zero new hardware. The ops team still asks me what changed."

Head of Manufacturing EngineeringAutomotive Assembly · 78 AMRs · Multi-Line

// Semiconductor Fab · 6-Axis Arms //

"Encoder drift was taking our robotic cells offline for 6–8 hours of recalibration every few weeks. VSI monitors positional accuracy at 10kHz, correlates drift with thermal expansion patterns, and triggers micro-recalibration at ±2 microns — without taking the cell down. Zero manual recalibration events in 11 months. For a Class 10 cleanroom, that is not a small thing."

Process Engineering DirectorSemiconductor Fab · Class 10 Cleanroom

// Consumer Electronics · Process AI //

"We had a 4.7% defect rate we could not explain. VSI's process AI found three hidden interaction effects — humidity, material lots, and tool wear state — that our SPC system had never flagged across two years of trying. It automated the corrections. Defect rate fell 45% in 90 days."

Quality DirectorConsumer Electronics · 96 Cobots · High-Volume

Integration Architecture

Connects to
Everything
You Already Run.

VSI AI integrates with your existing robotics stack, industrial protocols, SCADA systems, and enterprise software. No rip-and-replace. No IT project. Integration completed in weeks 1–2 of deployment.

Robotics & Industrial Protocols

ROS 2 — Robot Operating System 2

OPC-UA — Open Platform Communications Unified Architecture

MQTT / AMQP — Machine Telemetry Messaging

PROFINET / PROFIBUS — Siemens Industrial Ethernet

EtherNet/IP — Rockwell / Allen-Bradley

Modbus TCP/RTU — Legacy PLC Communication

IO-Link — Smart Sensor Interface

MTConnect — Machine Tool Data Standard

KUKA KRL API — KUKA Robot Language

Universal Robots URScript / PolyScope

Fanuc FOCAS / FOCAS2 API

ABB Robot Web Services (RWS)

Enterprise & Manufacturing Platforms

SCADA — Ignition, Wonderware, WinCC, iFIX

DCS — Honeywell Experion, Emerson DeltaV, ABB 800xA

MES — SAP Manufacturing Execution, Siemens Opcenter, Plex

CMMS — SAP PM, IBM Maximo, UpKeep, Fiix

ERP — SAP S/4HANA, Oracle Manufacturing, Infor CloudSuite

Historian — OSIsoft PI, Aspen InfoPlus.21, GE Proficy

Digital Twin — AVEVA, PTC ThingWorx, Azure Digital Twins

IIoT — AWS IoT, Azure IoT Hub, GE Predix

Condition Monitoring — SKF @ptitude, Emerson AMS, Fluke ii900

PLM — Siemens Teamcenter, PTC Windchill

Quality — ETQ Reliance, Intelex, MasterControl

WMS — SAP EWM, Manhattan Associates, Blue Yonder

Deployment Protocol

Sensor to Signal
to Prediction.
30 Days.

Structured deployment built for production environments — zero line disruption, parallel integration, measurable ROI before end of month one.

Fleet Telemetry Audit

Complete sensor coverage audit across all units. Telemetry gaps identified. Integration points mapped to existing SCADA, historian, and CMMS systems. Baseline OEE and MTBF captured for ROI benchmarking.

Week 1 · On-Site

Protocol Integration

VSI AI connected to existing infrastructure via approved industrial protocols — OPC-UA, Modbus, PROFINET, ROS2, MQTT. Data pipeline validated. Security controls applied. Zero production disruption throughout.

Weeks 2–3 · Remote + On-Site

Signature Learning & Go-Live

AI builds individual wear signatures for each unit from live telemetry. Baseline anomaly detection active within 7 days. Full predictive models calibrated to each machine's operating envelope within 30 days.

Week 4 · Model Activation

Continuous Intelligence

Monthly OEE and downtime trend reviews. Model retraining on new failure data. Fleet-wide pattern learning — when one unit's failure mode is learned, all 340 units benefit. Capability expansion as operations evolve.

Ongoing · Fleet-Wide

Predict Every Failure Before It Stops The Line.

Real-Time Data.Every Unit.Every Second.

Six Systems.Zero UnplannedDowntime.

Every Hour ofUnplannedDowntimeHas a Price Tag.

Every Sensor.Every Unit.Every Second.

ProductionOutcomes.Not Simulations.

Numbers YourPlant ManagerWill Print Out.

Connects toEverythingYou Already Run.