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Industrial Computer Vision.
Solving visual problems in high-noise environments.

Standard AI fails when things get dusty, dark, or messy.

I am a physicist. I build custom vision systems for heavy industry. My models work in high-noise environments (construction, mining, manufacturing, logistics) where off-the-shelf software breaks.

Scope of work (examples of past/current clients): Automated grading for luxury leather (micro-defect detection), biomass estimation in high-turbidity shrimp ponds, and high-speed sorting for organic agricultural products (cashews/coffee/rice).

No proprietary sensors required. Use existing or standard cameras. You get the code and the custom AI model. You own the IP. No subscriptions.

Defect Detection
Defect & Crack Detection
Catch failures early. Automatically spot micro-fractures, leaks, or material fatigue on the production line. I train models to see defects that human inspectors miss.
Cell Analysis Static Cell Analysis Animation
High-Noise/Turbid Analysis
See through the mess. My PhD research focused on tracking single cells in turbid, chaotic liquids. I apply that same physics-based logic to inspect pipelines, chemical flows, and messy industrial feeds.
Tracking Static Tracking Animation
Site Safety & Tracking
Track assets in real-time. Monitor vehicles, PPE compliance, and personnel entering hazard zones. Works in rain, fog, and low-light conditions where standard cameras fail.
Object Detection Optical Character Recognition
Logistics & Counting
Automate the paperwork. Instant counting of pipe segments, timber, or containers. High-speed reading of labels and serial numbers to eliminate manual entry errors.

Stop losing money on manual inspections and on large consultancy firms.

[ Phase 1: Feasibility ]
Zero cost. Send raw images/data. We verify if it's mathematically solvable within 48 hrs.
[ Phase 2: System Audit ]
We assess your physical environment (lighting, dust, edge-compute) and design the technical blueprint.
[ Phase 3: PoC Build ]
We build a lightweight model based on the audit specs to prove accuracy and inference speed.
[ Phase 4: Deployment ]
Production-grade pipeline deployed to your edge devices. You own the code and the IP.
Submit a Problem for Feasibility Review