INITIALISING SYSTEMS
SYSONLINE | EDGE NODES-- | LATENCY-- ms | UPTIME99.97% | UTC--:--:--
EST. 2026  ·  INDUSTRIAL AI

Machines that think. Systems that endure.
Intelligence built for the industrial edge.

AI SYSTEMS INDUSTRIAL SYSTEMS AUTOMATION EDGE COMPUTE

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< 4ms
Edge Inference Latency
99.97%
System Uptime Target
On-Device
No Cloud Dependency
IP Protected
Proprietary Core Technology
01 / ABOUT

About

IndSqr Systems builds advanced artificial intelligence for industrial environments — where reliability, speed and precision aren't features, they're requirements.

We develop at the intersection of machine learning, process-informed reasoning and edge compute — creating systems that operate autonomously in the field, without dependence on cloud infrastructure.

Several technologies currently under development are subject to intellectual property protection.

  • Industrial AI
  • Edge Intelligence
  • Automation Systems
  • Process-Aware Machine Reasoning
APPLICATIONS & SECURITY
Startup India
DPIIT — Application In Progress
SSL Secured
256-bit Encryption
02 / FOCUS

Focus Areas

Core pillars of our industrial AI platform — built for the demands of real manufacturing environments.

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01

Industrial AI

AI systems designed to monitor, interpret and respond to industrial processes in real time.

Real-time process intelligence.
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02

Edge Compute

Lightweight intelligence deployed directly at machine interfaces — no cloud required.

Intelligence where it matters.
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03

Automation

Adaptive automation architectures for intelligent, responsive manufacturing environments.

Systems that adapt and respond.
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04

Quality Intelligence

AI-driven quality inference systems that go beyond flagging anomalies — delivering actionable diagnostic output directly from machine signals.

From detection to decision.
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05

Process Monitoring

Continuous, real-time monitoring of manufacturing processes — identifying deviation from expected behaviour and supporting rapid corrective response.

Always watching. Always learning.
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06

Advanced Research

Process-informed AI systems and deep industrial reasoning engines under active development.

Science-driven, field-tested.
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03 / TIMELINE

Milestones

Q1 2025
R&D Lab & Team Formation
Dedicated research lab established. Core engineering and research hires onboarded across edge compute, ML systems and industrial process expertise.
Q3 2025
Edge Runtime — v0.1
First internal prototype of on-device inference runtime achieving sub-4ms latency on embedded hardware.
Q4 2025
IP Filing — Core Architecture
Provisional patent applications filed covering the core inference methodology and system architecture. IP protection currently in progress.
Q1 2026
Company Founded
IndSqr Systems incorporated. Core research agenda established around process-informed industrial AI.
Q1 2026
DPIIT Application & Early Engagements
Startup India recognition application submitted to DPIIT. Early engagement with industrial partners underway. Research collaboration pipeline open.
2026 — 2027
Field Deployment & Scale
Target: industrial pilot deployments, research partnerships and international market entry. Product v1.0 general availability planned.
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04 / TEAM

Team

Built by engineers and researchers with backgrounds in industrial systems, machine learning and applied physics.

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Founder & CEO
Industrial AI & Systems Architecture
Led development of embedded intelligence systems across industrial automation. Deep background in real-time control systems and applied ML.
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Head of Research
Industrial AI & Machine Learning
Former academic researcher in computational physics. Specialises in domain-informed model design and robust inference under industrial conditions.
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Head of Engineering
Edge Compute & Runtime Systems
Designed low-latency inference runtimes for resource-constrained hardware. Expert in firmware-level optimisation and edge deployment.
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Lead Data Scientist
Industrial Data & Signal Analysis
Specialist in industrial machine data and signal interpretation. Experienced in developing real-time inference pipelines for high-throughput manufacturing environments.
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Systems Engineer
Embedded Hardware & Integration
Experienced in deploying intelligence at the hardware boundary — from FPGA accelerators to microcontroller-class inference targets.
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Research Engineer
Automation & Systems Engineering
Background in industrial automation systems and applied machine learning. Bridges the gap between theoretical research and field deployment across manufacturing environments.
DRAG TO EXPLORE
ADVISORY BOARD

Guided by leading academics and domain experts spanning AI, industrial systems and applied engineering.

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ACADEMIC
Prof. Wajid Hussain
AI & Intelligent Systems
Distinguished researcher in artificial intelligence and autonomous systems. Brings deep expertise in applied machine learning for complex industrial and cyber-physical environments.
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ACADEMIC
Prof. Paul Libreitch
Advanced Manufacturing & Automation
Authority in advanced manufacturing processes and intelligent automation. Research spans the integration of AI and intelligent systems into production environments at scale.
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ACADEMIC
Prof. Cosmina Crutirou
Computational Modelling & Process Intelligence
Specialist in computational modelling and process-aware reasoning systems. Extensive research in applying advanced ML techniques to real-world industrial process optimisation.
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DOMAIN EXPERT
M K Ahmed
Industrial Operations & Systems Integration
Seasoned practitioner with extensive field experience across large-scale industrial operations. Provides strategic guidance on deployment feasibility, operational constraints, and system integration at the plant level.
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DOMAIN EXPERT
Md Nabeel
Edge Infrastructure & Industrial IoT
Expert in edge infrastructure design and industrial IoT ecosystems. Deep knowledge of connectivity, hardware constraints, and real-world deployment challenges across distributed industrial environments.
DRAG TO EXPLORE
05 / WHY INDSQR

Why IndSqr

Industrial AI is not a feature problem — it's an architecture problem. We build from first principles.

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01

Built for Industrial Reality

We don't adapt general-purpose AI to industrial settings. Our systems are designed from the ground up for the noise, variability and failure modes of real plant environments.

Purpose-built, not repurposed.
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02

Proprietary Core Technology

Our inference methodology is not adapted from general-purpose ML research. It is purpose-built for industrial environments, with foundational approaches currently under patent protection.

A technical moat, not just execution.
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03

No Cloud Dependency

Every inference runs on-device. No latency penalty, no connectivity requirement, no data leaving the facility. Designed for environments where cloud access cannot be assumed.

Sovereign, secure, on-premise.
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04

IP-Protected Core

Our foundational architectures are protected by active patent filings. The technical moat is structural — not just execution-dependent.

Defensible from the foundation up.
06 / INDUSTRIES

Target Industries

Built for manufacturing environments where process reliability, quality consistency and real-time intelligence directly determine output and cost.

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01

CNC Machining

Precision machining operations where tool wear, thermal variation and material batch differences demand continuous, real-time process intelligence to maintain quality without manual intervention.

Precision maintained at every cycle.
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02

Injection Moulding

High-throughput polymer processing lines where process consistency, material property variation and cycle-level fault detection determine both yield and downstream part quality.

Cycle-level quality, at production speed.
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03

Metal Forming & Stamping

Sheet metal and forging operations requiring rapid detection of material-embedded defects versus process-induced faults — enabling corrective action without scrapping entire batches.

Root cause clarity, not just anomaly flags.
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04

Multi-Process Facilities

Manufacturing sites running mixed process types that today deploy separate vendor systems per process. Our unified platform architecture is designed for exactly this — one inference engine, many processes.

One platform across the whole floor.
07 / RESEARCH

Research

Our work sits at the intersection of applied machine learning, manufacturing science and edge systems engineering. Core research directions are proprietary and subject to active IP protection.

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01

Manufacturing Intelligence

Research into AI-native approaches to manufacturing quality and process monitoring — operating directly from machine signals without dependence on external sensing hardware.

Intelligence from the machine itself.
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02

On-Device Inference

Developing model architectures and runtime systems capable of high-accuracy inference within the strict compute, memory and power constraints of embedded industrial hardware.

Maximum intelligence, minimum footprint.
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03

Cross-Process Scalability

Architectural research into unified inference platforms that generalise across diverse manufacturing process types — reducing the need for process-specific system deployments.

One platform, many processes.
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04

Actionable Diagnostics

Moving beyond anomaly flagging toward systems that generate diagnostically useful outputs — enabling operators to act decisively rather than investigate blindly.

Not just detection — direction.
Research collaboration enquiries welcome — get in touch
08 / CONTACT

Get in Touch

We work with industrial operators, research institutions and technology partners who are serious about the future of intelligent systems.

If that's you, we'd like to hear from you.

PARTNERSHIPS & RESEARCH
Response within 48 hours.