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Founder
Rohit Wakode — Founder & Director

B.Tech IIT Bombay · LLB GLC Mumbai. Building intelligent enterprise systems in India since 2014.

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Solution · 05

Computer Vision

Quality assurance, safety monitoring, and visual inspection systems for industrial and retail use.

CVSAMOCREdge

Cameras already watch your lines and floors; vision AI turns that footage into decisions. We build inspection and monitoring systems that catch defects, enforce safety, and read the physical world in real time.

Models are trained on your conditions, deployed where latency demands (often at the edge), and paired with an evaluation loop so accuracy holds as products and environments change.

WhatComputer-vision systems for quality, safety, and visual inspection.
Best forManufacturing lines, warehouses, retail floors, and sites.
RunsOn the edge for low latency; cloud-batched where it isn’t needed.
Time to valuePilot in 6–8 weeks.
01 — Capabilities

What we build.

Specific, production-grade capability — not a feature checklist.

/ 01

Defect detection

Surface, dimensional, and assembly defects caught on the line before they ship.

/ 02

Safety monitoring

PPE compliance, exclusion zones, and unsafe-behaviour alerts in real time.

/ 03

OCR & reading

Labels, serials, gauges, and documents read reliably in tough conditions.

/ 04

Edge inference

Optimised models on Jetson / industrial x86 for millisecond latency.

/ 05

Retail analytics

Shelf monitoring, footfall, and queue prediction from existing cameras.

/ 06

Drift monitoring

Accuracy tracked over time; retraining triggered before quality slips.

02 — How it works

From your problem to production.

01

Capture the conditions

02

Train for your reality

03

Deploy at the edge

04

Monitor & retrain

STEP 01

Capture the conditions

We collect and label data from your real environment — lighting, angles, and edge cases.

STEP 02

Train for your reality

Models tuned to your products and conditions, validated against held-out cases.

STEP 03

Deploy at the edge

Optimised inference on rugged hardware where latency matters; cloud where it doesn’t.

STEP 04

Monitor & retrain

Live accuracy monitoring catches drift and triggers retraining before quality drops.

03 — Where it pays

Use cases.

Line defect detectionDimensional inspectionPPE & safety complianceShelf & inventory monitoringSerial / label OCRFootfall & queue analytics
04 — Engineering

Stack & standards.

Models
Detection / segmentation
SAM-class models
OCR engines
Custom CNNs
Edge
NVIDIA Jetson
Industrial x86
TensorRT
Camera integration
Ops
Drift monitoring
Active-learning loop
Eval harness
05 — Outcomes

What good looks like.

ms
Edge latency
Decisions at line speed, not in the cloud.
24/7
Consistent inspection
No fatigue, no missed shift.
Adaptive
Holds accuracy
Drift monitoring + retraining.
06 — Questions

Answers, before you ask.

Can we use our existing cameras?
Usually yes. We work with your current camera feeds where resolution and placement allow, and advise on additions only where genuinely needed.
Does it run without internet?
Yes — edge deployment runs inference locally on rugged hardware, so the line keeps working even if connectivity drops.
How do you keep accuracy up over time?
We monitor live accuracy, detect drift as products and conditions change, and retrain on new edge cases through an active-learning loop.
Ready when you are

Put Computer Vision into production.

Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.

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