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Husain Bootwala

Mechatronics & Systems Engineer · Singapore

I design precision hardware and write the software that runs it.

Four years at KLA, taking semiconductor inspection equipment from NPI through production. Precision stages, motion control, DV/PV validation, and the field failure investigations that follow. Alongside that I build finished products on my own: a home-automation appliance with a local AI brain, a real-estate underwriting platform, and a spacecraft telecommand and telemetry stack.

4+ yrs
NPI & product engineering
50%
Test bench hardware cost reduction
$30k
Saved per field failure incident
50 hrs
Tool downtime prevented per incident
Projects running concurrentlyPeak 4 · 2025–2026
2026

On how I build

Writing code stopped being the difficult part some time ago. What separates engineers now is how well theyrun AI: the rules an agent has to work inside, whether the documentation still describes the code a month later, and which approvals are reserved for a person. Each project below documents how it was built as well as what it does.

Selected work

06 channels
2026 —

Mobile Particle Forensics Robot

A cobot that reaches inside semiconductor equipment to find where contamination starts

Particle contamination inside a wafer inspector is invisible until it kills yield, and finding its source means a human with a flashlight and a hunch. This is a UR5e-class arm on a rail base carrying a UV inspection head, with an LLM agent that proposes inspection sequences and executes them behind human confirm-gates.

  • UR5e / URScript
  • ROS 2 Jazzy
  • MoveIt 2
  • URSim (PolyScope)
  • Gazebo
  • +6
Active R&DHardware
2025 —

Nexop

A smart-home appliance that learns a household's routine and proposes the automations for it

Smart homes break down at the last step: the devices work, but writing the rules is a chore nobody keeps up with. Nexop learns recurring patterns from local device events and proposes automations the homeowner can approve, edit or ignore. Native Zigbee and KNX, Home Assistant for everything else, all of it running on a Jetson in the house rather than a vendor's cloud.

  • FastAPI
  • Next.js
  • TypeScript
  • PostgreSQL
  • Redis
  • +6
Active R&DHardware + Software
2025 —

NextDealIQ

Underwriting a rental property in minutes instead of an afternoon in a spreadsheet

Real-estate investors evaluate deals in fragile personal spreadsheets that break the moment the strategy changes. NextDealIQ models a property once and runs it through three underwriting strategies (long-term rental, BRRRR and flip) side by side, with the assumptions behind every number visible and editable. The software works; it is taking sign-ups ahead of launch.

  • Next.js
  • TypeScript
  • Prisma
  • PostgreSQL
  • Auth.js
  • +3
Active R&DSoftware
2026

Spacecraft TC/TM Link

A two-sided EGSE bench for a satellite power distribution unit

Ground support equipment for spacecraft integration: a subsystem simulator so ground software can be exercised before flight hardware exists, and a ground client so the subsystem can be exercised before the ground station does. The client commands a load, decodes the returned telemetry, evaluates it against safe operating limits, and logs every decision, including the fault case that you cannot safely produce on real hardware.

  • Python
  • Binary protocol design
  • Sockets
  • Docker Compose
  • CSV telemetry logging
ShippedSoftware
2025

Laundry Tracker

Barcode-tracked garment intake for a laundry that was running on memory and paper slips

A working laundry loses garments, and every lost garment is a customer argument with no evidence on either side. This tags each item with a printed barcode at intake, tracks it by household through the shop, prints a receipt, and messages the customer over WhatsApp. It is packaged as a desktop app because the shop counter has one Windows PC and no IT support.

  • FastAPI
  • Python
  • SQLAlchemy
  • Supabase / PostgreSQL
  • Jinja2
  • +3
ShippedSoftware
2026 —

Pharmacovigilance Productivity AI

Modelling a regulated industry's working day before committing to a product

Drug safety teams process adverse event reports under legal deadlines, and the obvious AI answer (automate the case processing) is the one regulators are least willing to accept. So I modelled the working day of eleven roles first, then registered and scored every place AI could add value against those models. I am not ready to choose a direction yet.

  • Domain research
  • Process modelling
  • Regulatory analysis
  • Opportunity scoring
  • Agentic research pipelines
In discoverySoftware