Altimetrik’s Industrial AI embeds intelligence into the physical world — from individual machines to full plant autonomy.

Operating Stack

Our 5-layer operating stack seamlessly integrates physical and manufacturing systems with AI/ML

Application

Plant copilot · Enterprise and Operator Interface Integration · Dashboards and Analytics

Platform

Agent orchestration, AI runtime, multi-agent coordination, Manufacturing AI platform

Intelligence

Perception Models · LLMs & SLMs · Edge AI Deployment · MLOps

Data

Real-time ingestion pipelines · Physical signal feature engineering · Ontology and knowledge graph · Storage

Physical

Sensor selection and calibration · Industrial protocol connectivity · Closed-loop edge control systems

What we build

Three service areas

Smart Machines

ML/AI-enabled autonomous machines

Individual machines that are self-aware, adaptive, and predictive — with autonomous control and an edge-native intelligence layer.

Machine Connectivity, Sensorization & Digital Twin

Machine data foundation, analytics, and reporting

Closed-Loop Optimization (Autonomous/Human at the helm through edge)

Predictive & Prescriptive Maintenance

Autonomous Plants

Factory and shop floor automation

Plant-wide perception-decision-action autonomy — zero-touch operations, OEE optimisation, quality intelligence, and cost control at scale.

Plant data foundation and integration

OEE, Throughput, Performance, and Quality Intelligence

Plant Copilot

Zero-touch ops

Intelligent Operations

Intelligence across the manufacturing value chain

AI across Supply chain and Service — from raw materials, to inventory management to customer service and beyond.

Service Intelligence

Supply chain Intelligence

Our approach

We help enterprises move beyond disconnected AI pilots

Transformation Vision Led

Define the future-state vision for Human at the helm, autonomous and intelligent operations

Economic Value Focused

Prioritize high-value use cases

Operational Outcomes Always

Deliver scalable improvements in manufacturing ops

Delivered through an operating model that:

01

Digitizes & simulates

Digitise operations, Mirror plants, lines, and workflows in a digital twin.

02

Closed-loop autonomy

Sense, decide, actuate, and learn continuously.

03

Optimizes in real time

Continuously tune physical workflows against operating KPIs.

04

Integrates enterprise apps

Connect ERP, MES, PLM, and edge systems into one fabric.

05

Keeps human at the helm

Operators stay in control with transparent, auditable autonomy.

Accelerators

Our accelerator stack across the value chain

Machine Intelligence

Closed-Loop Optimization Framework

Machines that get smarter with every run.

A full-stack framework spanning real-time OT data pipelines, continuous model retraining, and edge AI inference

Quality Intelligence

QualityIQ

Detect and prevent defects

Continuously captures OT and process data to learn your “golden process window” — then flags drift before quality failures happen, with real-time traceability, predictive alerts, and closed-loop correction recommendations.

Plant Intelligence

Production Copilot

Ask a question on plant’s performance. Get an answer in seconds.

Combines OT telemetry, maintenance history, and AI reasoning to deliver real-time diagnostics, root-cause insights, and operational recommendations

Service Intelligence

Intelligent Service Accelerator

Give every service engineer the knowledge of your best one.

GenAI-powered accelerator that surfaces relevant solutions from historical cases, manuals, and live tickets — cutting MTTR and improving first-time fix rates.

Measurable outcomes that we deliver

Alarm intelligence

~93%

Reduction in nuisance stoppages by classifying fault alarms in real time vs. legacy always-stop logic

Machine precision

−80%

Reduction in thermal positioning error through closed-loop edge AI on CNC machining centers

Cold chain ops

~60%

Reduction in operational costs and waste across 100,000+ refrigerated containers globally

Container availability

45%

Improvement in container availability through proactive fault detection across 40+ monitored factors

Store operations

200+

Manhours saved per store annually; 2,500+ retail locations shifted from manual to centralized monitoring

Yarn quality

82%

Prediction accuracy for forward and reverse yarn quality modeling across key spinning parameters

Machine downtime

90%

Reduction in equipment downtime through AI-powered service intelligence

Tool wear

50%

Accuracy in real-time tool condition classification — Non-wear, Wear, and Catastrophic — across materials

Appendix

Use cases

Real deployments across CNC machining, textile spinning, and service operations. Select a case to expand.

Thermal Error Compensation in CNC Machining

+

Machine deployed

Vertical Machining Center (CNC)

Our solution and how it works

ML model for thermal error prediction and compensation for Spindle position
Real-time temperature sensor and Fanuc controller parameters data is collected through edge device
ML model process the data in real-time on the edge device and predicts error
Then sends the error correction to Fanuc controller. Real time temperature and AI predicted correction values are also displayed on HMI screen.

Outcome

80% reduction in thermal positioning error (Z-axis: 69µm → 13µm; Y-axis: 27µm → 3µm)
ML model provides correction for every ±0.15°C temperature change
Minimum error prediction accuracy of ±0.1µm

Value delivered

80% reduction in thermal positioning error

15–20 min saved per machine warmup cycle

Consistent dimensional accuracy across component geometries

Tool Wear Prediction in CNC Machining

+

Machine deployed

Vertical Machining Center (CNC) — Stainless Steel, Steel, and Cast Iron applications

Our solution and how it works

ML model for tool/insert wear condition prediction, edge deployment pipe-line, MQTT protocol for data/write with Fanuc controller.
Real-time controller (Fanuc) parameters data is collected through edge device; ML model process the data in real-time on the edge device and classifies tool/insert condition.
Tool/insert condition classified in 3 categories: Non-wear, wear and catastrophic/severe.
Real time AI prediction of tool/insert condition is displayed on HMI screen for end-user to take an action.

Outcome

82% accurate prediction of tool wear condition SS, Steel and Cast Iron

Value delivered

Extended tool life through proactive intervention

Reduced cost per component via predictable insert life

Improved consumables inventory planning

Intelligent Service System

+

Solution built

GenAI based Intelligent Service system deployed on AWS
GenAI based RAG Architecture
Re-ranking module, Backend Module, Database, Frontend Module
APIs for accessing live SR ticket data, APIs for accessing closed ticket SR, DEMS data.

How it works

RAG (Retrieve and Generate) approach uses the historical SR, DEMS, LMW Manuals, WhatsApp data to find the most relevant solutions for a live ticket issue.
When a service engineer asks a question, the AI solution retrieves similar cases from past tickets and provides multiple solution suggestions.

Outcome

Chatbot application on LMW AWS Cloud
Role based access for users: Admin/HO and LMW’s Service Engineer
Application has Admin page for performance monitoring and Service Engineer page has chatbot feature.

Value delivered

Reduction in Mean Time To Repair (MTTR) from 6-8 hours to 3-4 hours.

Improvement in First Time Fix Ratio (FTFR) from the current 80% - 85%

2x numbers of tickets addressed per day

Forward and Backward Yarn Quality Prediction in Textile Spinning

+

Solution built

Web app dashboard with integrated chatbot (AWS-based deployment)
Predict yarn quality based on input quality and machine settings
Predict machine settings and input quality based on required output quality

How it works

Collects raw material properties, machine settings, and process parameters
Predicts yarn quality metrics (CSP, RKM, U%, IPI) using trained ML models
Provides recommendations for optimal machine settings and raw material selection
Accessible via chatbot for both technical and non-technical users

Outcome

Forward Prediction: 90.48% accuracy
Reverse Prediction: 90% accuracy
Out of 13 critical yarn parameters, 11 parameters observed significant correlation

Value delivered

Optimized machine settings across customer base

Minimized quality variation across production runs

Enabled sales teams to answer raw material and settings questions independently

Alarm Severity Prediction in Textile Manufacturing

+

Machine deployed

Finisher Draw Frame (Textile Spinning)

Outcome

82% accuracy in detecting true fault alarms
~93% reduction in nuisance stoppages vs. legacy “always-stop” logic
128ms average inference latency — well within the 400ms operational budget

Outcome

Built an ML model trained on historical alarm logs from PLC and SpinConnect, labeling alarms as True (critical) or False (nuisance) using rule-based logic and operator sequences
Deployed on an edge device with OPC-UA integration — classifying each alarm in real time and sending automated severity commands (STOP, WARN, or IGNORE) directly to the PLC

Value delivered

Higher MTBA through reliable alarm classification

Reduced operator fatigue and improved fault clarity

Tool Wear Prediction in CNC Machining

+

Solution built

Vertical Machining Center (CNC) — Stainless Steel, Steel, and Cast Iron applications

Our solution and how it works

ML model for tool/insert wear condition prediction, edge deployment pipe-line, MQTT protocol for data/write with Fanuc controller.
Real-time controller (Fanuc) parameters data is collected through edge device; ML model process the data in real-time on the edge device and classifies tool/insert condition.
Tool/insert condition classified in 3 categories: Non-wear, wear and catastrophic/severe.
Real time AI prediction of tool/insert condition is displayed on HMI screen for end-user to take an action.

Outcome

82% accurate prediction of tool wear condition SS, Steel and Cast Iron

Value delivered

Extended tool life through proactive intervention

Reduced cost per component via predictable insert life

Improved consumables inventory planning

Sliver Quality Optimization During Acceleration & Deceleration

+

Machine deployed

Finisher Draw Frame — 1 Servo & 2 Servo configurations

Outcome

Root cause identified: 65–100ms roller startup lag and 36:1 draft ratio surge
Corrective solution validated: EMA-based voltage correction + multi-segment ramping

Our solution and how it works

Built a diagnostic analysis pipeline capturing high-resolution RPM and draft ratio signals via encoders and LVDTs — identifying a 65–100ms timing lag between front and middle rollers causing transient tension imbalance and sliver thinning
Diagnosed draft ratio surging to 36:1 at startup, causing 6–8m mass dips; explored multi-segment acceleration ramps, PID retuning, and EMA-based voltage correction to eliminate overshoot and sensor drift
Delivered as upgraded PLC logic with multi-segment ramping and PID tuning for smooth roller synchronization across Acc/Dec cycles

Value delivered

Eliminated 6–8m sliver mass dip at startup

0.5–1.2% reduction in clearer cuts

Consistent yarn quality across production runs

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