Applied AI · Vision-guided robotics
Vision-guided bin picking at 80 ms end-to-end
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.
Applied AI & ML
Applied AI & Machine Learning Services
We build AI that ships with a product, not AI that sits in a slide deck. Our models run on robots, on embedded boards, and inside the engineering workflows that move real work forward.
What we do
Practical support for targeted engineering work
Yantrix designs, trains, optimizes, and deploys machine-learning systems for robotics, embedded products, and industrial engineering. We handle the full lifecycle — data strategy, labelling, model selection, training at scale, quantization and hardware-specific optimization, deployment, and MLOps — with a deliberate focus on real-world physical deployment.
What problems we solve
- Move from prototype notebooks to models that run reliably on production hardware.
- Deploy computer vision and perception directly on robots, cameras, and PCBs — not in the cloud.
- Cut simulation and design-exploration time with ML surrogates and generative workflows.
- Augment engineering teams with LLM copilots that interact with CAD, simulation, and documentation.
Tools we use
- PyTorch
- TensorFlow
- Ultralytics YOLO (v8 / v11)
- OpenCV
- TensorRT
- ONNX Runtime
- TFLite / TFLite Micro
- OpenVINO
- NVIDIA Jetson (Nano / Orin)
- Google Coral
- Hugging Face Transformers
- LangChain
- Weights & Biases
Deliverables
- Trained and validated models with reproducible training pipelines
- Hardware-accelerated deployment binaries (TensorRT / ONNX / TFLite)
- Integration with ROS 2 nodes, firmware, or product APIs
- Performance benchmarks — latency, accuracy, memory, power
- MLOps handoff: retraining pipeline, monitoring, failure-case tracking
Use cases
Industries where this service applies
We adapt the same engineering service to different product contexts depending on the load case, packaging problem, validation target, or deployment environment.
Robotics
Relevant when the project needs focused applied ai & machine learning support.
industrial automation
Relevant when the project needs focused applied ai & machine learning support.
IoT devices
Relevant when the project needs focused applied ai & machine learning support.
UAV and drones
Relevant when the project needs focused applied ai & machine learning support.
consumer electronics
Relevant when the project needs focused applied ai & machine learning support.
manufacturing QA
Relevant when the project needs focused applied ai & machine learning support.
Related work
Case studies connected to this service
These links help visitors move from service intent to real examples of engineering work.
Edge AI · On-device inspection
Zero-cloud defect detection camera on ESP32-S3
A production conveyor inspection camera running a quantized INT8 CNN entirely on an ESP32-S3 — 18 FPS at 0.4 W, no cloud, 6× lower capex per station.
ML-accelerated engineering
500× faster topology exploration with an ML-surrogate FEA
A physics-informed neural network trained on 12,000 ANSYS runs replaces the full solver for early-stage topology — predicts stress fields in 40 ms vs. 22-minute solves.
From the blog
Articles that support this service topic
Technical articles give Google more paths into the service pages and help visitors explore adjacent engineering questions before they get in touch.
3D Printing
3D Printing Services in India: How Product Teams Build Better Prototypes Faster
Learn how 3D printing services help startups and manufacturers in India validate CAD designs, reduce prototyping cost, and build functional parts faster.
Applied AI
Deploying YOLOv11 to Jetson Orin Nano at 30 FPS
Walkthrough of shipping a segmentation-class YOLOv11 model to a Jetson Orin Nano at production latency — quantization, TensorRT conversion, and the pitfalls.
Simulation
Thermal analysis for electronics enclosures
How CFD-based thermal analysis catches hotspots, airflow dead zones, and IP67-versus-cooling trade-offs in electronics enclosures before the first prototype ships.
FAQ
Questions teams ask before they engage
Service-specific questions are useful for both users and search visibility around intent-driven queries.
Do you only work on edge / embedded AI?
No. Edge and on-device is our signature because it's where most teams struggle, but we also build and deploy models that run on servers, GPUs, or cloud — whichever makes sense for the product.
Can you take a model we already have and deploy it to our device?
Yes. A large part of what we do is model optimization — quantization (INT8, FP16), graph surgery, TensorRT / ONNX conversion, and hardware-specific acceleration so an existing model runs fast and small on the target chip.
How do you validate model accuracy for production?
Held-out test sets, confusion matrices per class, edge-case sweeps, slice-based evaluation, and field-trial data collection. We write the evaluation harness before we celebrate any accuracy number.
Can you work under NDA on proprietary data?
Yes. We routinely work with confidential CAD, vision datasets, and product telemetry. NDAs and private data pipelines are part of most engagements.
Start your project
Need applied ai & machine learning support?
Send the problem, your current design stage, and any existing files. We can scope the work from there.