01 / AI vision
Teach a camera to see.
Image capture, object detection, coordinate frames. From spotting a coloured block to measuring orientation on a real part.
Hands-on AI vision, robotics and automation — for the engineering colleges, polytechnics and schools training tomorrow’s factory engineers.
01 · For colleges
For engineering colleges, polytechnics and vocational institutes building Industry 4.0 capability. Real robots, real vision systems, real cycle data — not a simulator.
What’s included
Robot arm, vision module, weld or pick-and-place workstation, software stack, and a curriculum tied to mechanical, electrical and CSE syllabi.
Who it’s for
B.Tech and diploma programmes in mechatronics, manufacturing, robotics and CSE. Faculty wanting a single lab that spans vision, control and data.
Why it matters
India’s factories are hiring engineers who’ve only seen automation in slides. The skill gap is the bottleneck. The lab closes it before campus placements.
02 · For schools
For grades 6 – 12, scaled to the classroom. Exposure-level workstations students can run themselves, with STEM modules that fit into a regular school week.
What’s included
Compact desktop robot, camera kit, drag-and-drop software, and a project workbook of 20+ guided experiments aligned to NEP-aware STEM outcomes.
Who it’s for
CBSE, ICSE and state-board schools investing in atal-tinkering-style labs. Computer-science and physics teachers who want a hands-on track, not another screen.
Why it matters
Children who build a working pick-and-place at fourteen pick STEM streams with intent. The lab shifts the conversation from “coding” to “making things move”.
03 · What students learn
Three pillars run across both age groups, scaled in depth. The same vocabulary an engineer hears on the shop floor — introduced in a lab where they can actually run it.
01 / AI vision
Image capture, object detection, coordinate frames. From spotting a coloured block to measuring orientation on a real part.
02 / Robotics
Joints, kinematics, end-effectors, safety envelopes. Pick-and-place by week two, vision-driven trajectories by week six.
03 / Automation
Sensors in, decisions out, data logged. Students design a small line, run it, and read what the cycle data says.
04 · Get in touch
Tell us your programme, the space you have and the cohort size. We’ll come back with a lab spec, a budget and an installation timeline — usually inside a working week.