In most factories, a tiny change in shape, lighting, or material can bring an entire production line to a halt. Humans adapt in seconds. Machines do not. CyLnr exists to close that gap, using neuroscience to give robots something close to human-like perception instead of brittle, rule-based automation.
Let us look at what this startup is actually trying to build.
The problem no one has cracked
Modern factories are filled with robots that work with extreme precision. But change a material, lighting condition, or object shape, and the system breaks. Everything has to be reprogrammed.
In contrast, a primate in an IISc neuroscience lab can see an unfamiliar object and understand how to interact with it within seconds. No retraining. No downtime. CyLnr is trying to give robots that same perceptual ability. This gap between mechanical automation and intelligent perception is one of the biggest unsolved problems in industrial robotics.
What CyLnr is building
Founded in 2019 by Gokul N A and Nikhil Ramaswamy, CyLnr set out to create robots that can understand what they are looking at, not just execute instructions. Their core platform, CyRo, gives robots visual intelligence so they can recognise objects, adapt to new materials, and work without constant reprogramming.
The long-term vision is what they call a universal factory: one robotic system that can handle infinite variations without stopping. By November 2024, CyLnr had raised a Rs 830 crore Series A round, built centres in Bengaluru and Switzerland, and was scaling towards manufacturing one robot system per day.
Why neuroscience is their moat
Most robotics companies focus on better cameras and better software. CyLnr went deeper. They partnered with IISc’s Centre for Neuroscience and Vision Lab to study how primate brains process shapes, textures, and motion. Instead of writing rules for every object, CyLnr translates biological perception models into algorithms that allow robots to interpret what they see.
This shifts automation from following instructions to making decisions. This approach creates a powerful moat. You cannot copy it by hiring more engineers. It requires years of neuroscience research, institutional partnerships, and continuous biological learning.
A business model built for scale
CyLnr does not just sell robots. It sells intelligence.
Traditional robotics companies sell expensive machines once. CyLnr layers recurring software subscriptions on top of its hardware through its visual intelligence platform. Every robot also feeds data back into the system, making the entire network smarter over time.
This Robotics as a Service model creates predictable recurring revenue, strong customer lock-in, and margins that look more like SaaS than manufacturing.
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Why is the timing right?
India’s robotics market is growing rapidly as manufacturing expands and labour costs rise. At the same time, vision-based automation is becoming mainstream globally, and deep tech funding in India is flowing back into hardware and IP-heavy companies. CyLnr sits exactly at the intersection of these trends, with policy support, market demand, and capital all aligned.
The bigger insight
CyLnr shows what happens when deep science meets real-world industry. Neuroscience plus robotics creates something that competitors cannot easily replicate. They are not just building better robots. They are building the operating system for adaptive manufacturing.
Want to go deeper? Read the full CyLnr case study to explore their technology, IISc research partnership, revenue model, and market strategy in detail. Click here to access the complete study.
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