INITIALISING SYSTEMS
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EST. 2026  ·  INDUSTRIAL QUALITY INFERENCE

Root cause. Physical location. Corrective pathway.
From existing machine signals — no sensor retrofit required.

QUALITY INFERENCE AMCS PLATFORM EDGE COMPUTE PATENT PENDING

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On-Device
Edge Inference — No Cloud
Zero
Cold-Start Period Required
Read-Only
No Machine Modification
IP Protected
Proprietary Core Technology
01 / ABOUT

About

IndSqr Systems develops AMCS — the Adaptive Manufacturing Control System — a patent-pending industrial quality inference platform that determines the root cause, physical location, and corrective pathway for manufacturing anomalies in real time, primarily from existing machine signals.

We build at the intersection of physics-based process modelling, engineering-grounded inference, and edge compute — creating systems that operate on-premises without cloud dependency, from the first production cycle without a training period.

Core system concepts and methodology protected under Provisional Patent Application No. 202641009757, filed with the Indian Patent Office.

  • Industrial Quality Inference
  • Physics-Constrained Process Modelling
  • Edge-Native Deployment
  • Sensorless Machine Monitoring
APPLICATIONS & SECURITY
Startup India
DPIIT Recognised · DeepTech · DIPP258192
SSL Secured
256-bit Encryption
02 / FOCUS

Focus Areas

Core pillars of the AMCS quality inference platform — built for the demands of real manufacturing environments.

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01

Quality Inference

Physics-constrained inference that classifies manufacturing anomalies by root cause and workpiece location — not just detection, but diagnosis and decision guidance.

From anomaly flag to corrective action.
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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

Closed-Loop Control

Governed autonomous response to classified anomalies — parameter correction, part isolation, or maintenance alerts — delivered through an auditable, operator-overridable control layer.

Inference drives response. Operator governs it.
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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
Research & Concept Development
Research into sensorless manufacturing quality inference initiated. Core system concept and problem definition developed across physics-based process modelling and industrial AI.
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 — Provisional Patent
Provisional patent application No. 202641009757 filed with the Indian Patent Office, covering the core inference methodology, classification framework, and system architecture.
Q1 2026
Company Founded
IndSqr Systems incorporated. Core research agenda established around process-informed industrial AI.
Q1 2026
DPIIT Recognised — DeepTech
IndSqr Systems granted DPIIT Startup India recognition under the DeepTech category (DIPP258192). Udyam registration obtained. SISFS seed fund application in progress.
2026 — 2027
Phase 1 — Prototype & Pilot
Target: inference engine prototype complete, CNC signal adapters validated, classification accuracy confirmed on production data, non-provisional patent filed, first no-cost pilot deployments initiated.
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04 / TEAM

Team

Founders with backgrounds in artificial intelligence, industrial systems, and commercial operations.

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Malik Naseruddin
Managing Director & Co-Founder
MSc Artificial Intelligence. Led full AMCS system architecture, IP strategy, and provisional patent filing. Responsible for product direction, technical architecture, and industrial systems design.
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Tahiya Shariff
Director & Co-Founder
BVA. Responsible for operations, commercial strategy, business development, and visual communications. Co-founder since incorporation, March 2026.
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ADVISORY BOARD

Guided by confirmed academic advisors and domain experts spanning mechanical engineering, chemical engineering, civil engineering, electronics and communications engineering, and industrial operations.

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ACADEMIC
Dr. R. Noor Ahmed
Mechanical Engineering
Mechanical Engineering academic at Anjuman Engineering College. Provides domain guidance on manufacturing process mechanics and engineering-physics foundations underlying the AMCS platform.
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ACADEMIC
Prof. Wajid Hussain
Mechanical Engineering
Mechanical Engineering faculty at Yanbu Industrial College, Royal Commission, KSA. Advises on mechanical systems, manufacturing processes, and industrial deployment environments.
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DOMAIN EXPERT
Sameer Hussain
Chemical Engineering — Industrial Systems
Chemical engineering background with domain expertise in industrial process systems. Advises on process chemistry, material behaviour, and industrial operations relevant to quality inference deployment.
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DOMAIN EXPERT
Aamir Hussain
Mechanical Engineering — Operations
Mechanical engineering practitioner with experience in engineering operations and systems integration. Advises on manufacturing process feasibility and operational deployment constraints.
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DOMAIN EXPERT
Md Nabeel
Civil Engineering — Infrastructure
Civil engineering background with deep knowledge of infrastructure design and project management. Advises on physical deployment environments, facility constraints, and industrial infrastructure requirements.
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DOMAIN EXPERT
M K Ahmed
Mechanical Engineering — Industrial Operations
Mechanical engineering practitioner with extensive field experience in large-scale industrial operations. Provides strategic guidance on deployment feasibility and system integration at the plant level.
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DOMAIN EXPERT
Salim Mallik Shariff
Electronics & Communications Engineering
Electronics and communications engineering background with expertise in manufacturing and business operations. Advises on systems integration, signal infrastructure, and commercial deployment strategy.
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

Physics-Derived, Not Data-Trained

AMCS derives expected process behaviour from manufacturing physics — material properties, process mechanics, and engineering relationships — not from statistical aggregation of prior workpieces. Operates from the first production cycle without a training period.

Zero cold-start. Immediate intelligence.
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02

Spatial Intelligence

AMCS determines not just that an anomaly occurred, but where on the workpiece — and whether that location affects the final machined geometry. Anomalies in regions removed by subsequent machining require no action. This eliminates unnecessary part scrap.

Location matters. AMCS knows it.
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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

Fault-Safe Adaptive Learning

AMCS refines its expected behaviour models only from production cycles confirmed acceptable through independent quality inspection — tracking legitimate process evolution without incorporating fault conditions into its expectations.

Learns only from good cycles.
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 research sits at the intersection of physics-based process modelling, manufacturing science, and edge systems engineering. Core methodologies are proprietary and protected under filed patent applications.

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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

Quality-Gated Adaptive Learning

Research into inference systems that refine expected behaviour models exclusively from production cycles confirmed acceptable through independent physical quality inspection — tracking legitimate process evolution without incorporating fault conditions.

Learns from good cycles only.
Research collaboration enquiries welcome — get in touch
08 / CONTACT

Get in Touch

We work with precision manufacturers, CNC operators, and industrial partners interested in no-cost pilot validation of the AMCS platform on their own production equipment.

Research institutions and technology partners are also welcome. If that's you, we'd like to hear from you.

PARTNERSHIPS & RESEARCH
Response within 48 hours.