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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 · 11

Robotics

Perception, planning, and control stacks for cobots, mobile robots, and inspection systems.

ROS 2SLAMSim2RealEdge AI

We build the systems that move atoms, not just bits. From perception and SLAM to motion planning and control, we engineer robotics stacks on ROS 2 that hold up on a real factory floor.

Sim-to-real done properly — domain-randomised training in Isaac Sim/Gazebo — gets you to reliable behaviour faster and safer than tuning on live hardware.

WhatPerception, planning, and control stacks for cobots and mobile robots.
Best forManufacturers and operators automating physical tasks.
RunsOn industrial-grade edge hardware.
Time to valueProof-of-capability in weeks.
01 — Capabilities

What we build.

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

/ 01

Perception

ML vision and sensor fusion for detection, pose, and tracking in cluttered scenes.

/ 02

Navigation & SLAM

ROS 2 navigation and mapping for mobile robots in dynamic environments.

/ 03

Motion planning

MoveIt-based planning with safety envelopes for cobots and arms.

/ 04

Sim-to-real

Domain-randomised training in Isaac Sim / Gazebo for reliable transfer.

/ 05

Edge deployment

Industrial-grade inference on Jetson, ruggedised x86, or custom hardware.

/ 06

Integration

Bridges to PLCs, MES, and your existing line control.

02 — How it works

From your problem to production.

01

Define the task

02

Build perception & planning

03

Train in simulation

04

Deploy & integrate

STEP 01

Define the task

We scope the physical task, environment, and safety constraints, and agree acceptance tests.

STEP 02

Build perception & planning

Develop the perception, navigation, and planning stack on ROS 2.

STEP 03

Train in simulation

Domain-randomised sim training de-risks behaviour before touching hardware.

STEP 04

Deploy & integrate

Run on rugged edge hardware, integrated with your line and safety systems.

03 — Where it pays

Use cases.

Cobot pick-and-placeAutonomous mobile robots (AMR)Inspection robotsBin pickingLine integrationSim-to-real programmes
04 — Engineering

Stack & standards.

Robotics
ROS 2
MoveIt
Nav2
SLAM
Simulation
NVIDIA Isaac Sim
Gazebo
Domain randomisation
Edge
Jetson
Ruggedised x86
PLC / MES bridges
05 — Outcomes

What good looks like.

Real-floor
Not lab-only
Stacks that survive production.
Safer
Sim-first
Behaviour proven before live hardware.
Integrated
Into your line
Bridges to PLC/MES.
06 — Questions

Answers, before you ask.

Do you build robots or the software?
Primarily the intelligence — perception, planning, and control — integrated with cobots and robots from established hardware vendors, plus custom hardware where needed.
Why simulate first?
Domain-randomised simulation lets us reach reliable, safe behaviour far faster and cheaper than tuning on live hardware, then transfer it to the real robot.
Can it integrate with our existing line?
Yes — we bridge to your PLCs, MES, and safety systems so the robot is part of the line, not an island.
Ready when you are

Put Robotics 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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