Vision systems win or lose on integration. We build perception stacks where the model, the embedded hardware, the robot, and the physical engineering decision all line up for global and domestic teams.
What we do
YantriX delivers computer-vision systems for vision-guided pick-and-place, bin picking, defect detection on conveyors, SKU recognition, and robotic manipulation. We design the data pipeline, pick and train the model (YOLO detection, SAM-2 segmentation, custom classifiers), integrate it into ROS 2 nodes or a PLC-facing service, and ship benchmarks against the target FPS and accuracy envelope.
Share your technical requirements, 3D CAD files, or operating specs. NDA support is available where required before confidential file exchange.
We adapt the same engineering service to different product contexts depending on the load case, packaging problem, validation target, or deployment environment.
Kinematic chain modeling, dynamic joint stiffness optimization, perception camera mounts, and rapid physical prototype validation.
Conveyor SKU identification, autonomous pallet docking, and high-speed bin-picking perception pipelines.
Sub-millimeter edge defect detection, automated dimensional verification, and solder joint optical inspection.
High-speed blister pack inspection, carton barcode tracking, and pick-and-place delta robot vision guidance.
SMD component placement verification, PCB trace defect detection, and robotic screw fastening inspection.
Crop health vision inspection, robotic harvesting gripper guidance, and solar-powered outdoor perception units.
See how this engineering capability applies across real project scopes, analysis goals, and physical prototype iterations.
Applied AI · Vision-guided robotics
How a YOLOv11-Seg + 3D-pose stack on a Jetson Orin Nano replaced fixed-pose jigs in a 6-DOF robotic cell — sub-80 ms latency, 99.2% accuracy, 40% throughput gain.
Robotics · Mechanical Design
Mechanical design of a 6-DOF compact robotic arm with cycloidal gearboxes hitting ±0.1 mm repeatability at 5 kg payload — from kinematic chain to manufactured prototype.
Explore practical guides covering robotics development, perception, mechanical integration and prototyping.
Applied AI
A practical guide to computer vision development services in India — what use cases work, how vendors price, and what production deployment really looks like.
Edge AI
The production playbook for deploying YOLOv8 / v11 on Jetson Nano / Orin in Indian factories — quantization, TensorRT, OTA updates, monitoring, and the gotchas you only learn from shipping.
Applied AI
Walkthrough of shipping a segmentation-class YOLOv11 model to a Jetson Orin Nano at production latency — quantization, TensorRT conversion, and the pitfalls.
Common questions teams ask before starting a project.
On a Jetson Orin Nano, we typically ship YOLOv11-Seg pipelines between 12-30 FPS end-to-end, with decision latency under 100 ms including camera capture, inference, and robot command. Specific numbers depend on image resolution and the target class count.
That's the default. We usually start with a base detector, then fine-tune on a small labelled dataset of your parts. We'll set up labelling tooling and hand you a retraining pipeline so you can keep extending it.
Yes. Vision fails most often because of the physical setup, not the model. We scope the camera, lens, lighting, and mount geometry as part of the engagement.
Tell us about your kinematics, payload, ROS 2 requirements, or autonomous navigation targets to start scoping your robotics build.