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- LLM Engineering & Fine-Tuning
- View All LLM Engineering & Fine-Tuning
- Fine-Tune Industrial Domain LLMs 12x Faster with Unsloth and Hugging Face TRL
- Extract Structured Equipment Diagnostics from LLMs with DSPy and Instructor
- Optimize Industrial Knowledge Base Retrieval with LlamaIndex and DSPy
- Retrieve Equipment Documentation with LangChain RAG and 4-Bit Quantized Models
- Align Manufacturing Domain LLMs with RAG and Reinforcement Learning Feedback
- Semantically Search Equipment Specifications with Neo4j Knowledge Graphs and Transformers
- Quantize Industrial LLMs with PEFT and Unsloth Studio for Edge Deployment
- Align Industrial LLMs with RLHF and Hugging Face TRL for Manufacturing Use Cases
- Fine-Tune Domain-Specific LLMs with LLaMA-Factory and Axolotl for Manufacturing Workflows
- Fine-Tune Industrial Vision-Language Models on Apple Silicon with MLX-VLM and Hugging Face Transformers
- Generate Structured Compliance Reports from LLMs with Instructor and LangChain
- Train Domain-Specific Manufacturing LLMs with torchtune and Weights & Biases
- Build RAG Pipelines for Equipment Maintenance Manuals with LlamaIndex and LangChain
- Fine-Tune Quantized LLMs on Industrial Data with bitsandbytes and TRL
- Adapt Domain-Specific Language Models with PEFT and TRL
- Evaluate Fine-Tuned Factory LLMs with Structured Output Validation using Axolotl and Instructor
- Build GRPO Post-Training Pipelines for Industrial Quality LLMs with TRL v1.0 and DSPy
- Fine-Tune Industrial Domain LLMs from YAML Config with LLaMA-Factory and PEFT
- Industrial Automation & Robotics
- View All Industrial Automation & Robotics
- Train Robotic Manipulation Policies with LeRobot and Isaac Lab
- Simulate Factory Robot Grasping with MuJoCo Playground and JAX
- Plan Collision-Free Industrial Robot Paths with MoveIt 2 and NVIDIA cuMotion
- Test Warehouse Robot Fleets with ROS 2 Nav2 and Gazebo Simulation
- Train Vision-Language-Action Robot Policies in NVIDIA Isaac Sim with LeRobot
- Train Robot Grasping Policies with PyBullet Physics and TensorFlow Reinforcement Learning
- Coordinate Heterogeneous Robot Fleets with Nav2 and Open-RMF
- Control Industrial Robot Actuators in Real Time with ROS 2 Control and MoveIt 2
- Develop Robotic Manipulation Skills with PEFT-Optimized Policies and Isaac Lab
- Simulate Multi-Robot Factory Coordination with Gazebo and Open-RMF
- Control Industrial Robots via Natural Language with ROS-LLM and FastAPI
- Build Edge Robotic Control Systems with micro-ROS and ros2_control
- Train Robotic Assembly Skills in Simulation with robosuite and Stable-Baselines3
- Train Factory Floor Navigation Agents with MuJoCo and Stable-Baselines3
- Train Robotic Pick-and-Place Policies from Demonstrations with LeRobot and robosuite
- Plan Robot Arm Trajectories for Assembly Tasks with MoveIt2 and Gazebo
- Automate Warehouse AMR Navigation with Nav2 and ros2_control
- Execute GPU-Planned Industrial Robot Trajectories in Real Time with cuRobo and ros2_control
- Fine-Tune GR00T Robot Policies for Industrial Grasping with Isaac GR00T and Isaac Lab
- Wrap Physical Factory Robots as RL Training Environments with Gymnasium and rclpy
- Simulate and Validate Industrial Robot Arm Paths with PyBullet and MoveIt2
- Digital Twins & MLOps
- View All Digital Twins & MLOps
- Build Industrial Equipment Twins with Siemens Composer and MLflow
- Monitor Assembly Line Health with Evidently and YOLO26
- Orchestrate Robotics Pipelines with OpenALRA and Kubeflow
- Build Digital Twins for Automotive Electronics with Synopsys eDT and MLflow
- Validate Manufacturing Data Pipelines with Great Expectations and DVC
- Accelerate Digital Twin Data Collection with Azure Digital Twins SDK and Weights & Biases
- Version Sensor Data with DVC and Vertex AI SDK
- Orchestrate Twin Deployments with Kubeflow and AWS IoT TwinMaker SDK
- Track Twin Model Performance with Weights & Biases and AWS IoT TwinMaker SDK
- Automate Pipeline Workflows with ZenML and Azure Digital Twins SDK
- Track Digital Twin Model Drift with Evidently and MLflow
- Validate Twin Simulation Outputs with Great Expectations and Vertex AI SDK
- Automate Digital Twin Retraining Pipelines with ZenML and Weights & Biases
- Monitor ML Pipeline Drift for Digital Twin Models with Evidently and ZenML
- Sync Industrial Digital Twin State to MLOps Pipelines with AWS IoT TwinMaker SDK and ZenML
- Track Factory Model Experiments Across Sites with MLflow and DVC
- Unify Experiment Tracking and Model Versioning for Digital Twins with Weights & Biases and MLflow
- Orchestrate Industrial Model Retraining Pipelines for Digital Twins with Kubeflow and MLflow
- Gate Digital Twin Retraining on Sensor Data Quality with Evidently and Vertex AI SDK
- Computer Vision & Perception
- View All Computer Vision & Perception
- Detect Casting Defects with YOLO26 and MetaLog
- Segment Welding Flaws in Video Streams with SAM 2 and Supervision
- Train Edge Vision Models with Qwen2.5-VL and ZenML
- Classify Manufacturing Defects with GLM-4.5V and Weights & Biases
- Detect Quality Defects in Video Streams with Grounded SAM 2 and Supervision
- Enable 3D Manufacturing Perception with InternVL3 and Roboflow Inference
- Recognize Industrial Components with GLM-4.5V and Hugging Face Transformers
- Recognize Equipment Components with CLIP and OpenCV
- Segment Industrial Defects with Florence-2 and Detectron2
- Detect Open-Set Objects with Grounding DINO and DVC
- Build Compact Industrial Vision Encoders with EUPE and OpenCV
- Detect Factory Defects via Text Prompts with SAM 3 and Roboflow Inference
- Extract Visual Embeddings for Manufacturing Quality with Perception Encoder and Ultralytics
- Accelerate Video Annotation for Manufacturing with Grounding DINO and Supervision
- Detect Assembly Line Defects in Real Time with Ultralytics and Supervision
- Extract Structured Measurements from Factory Floor Footage with Florence-2 and OpenCV
- Segment Defective Components in Quality Inspection with SAM 2 and Supervision
- Classify Industrial Components with Zero-Shot Vision Using CLIP and Detectron2
- Deploy Open-Vocabulary Factory Defect Detection to the Edge with Florence-2 and Roboflow Inference
- Inspect Industrial Parts with Zero-Shot Multimodal Vision using Gemma 4 and Supervision
- Filter Production Line Detections by Semantic Similarity with CLIP and Supervision
- Generate Spatial Quality Heatmaps for Industrial Surfaces with Perception Encoder and Supervision
- Multi Agent Systems
- View All Multi Agent Systems
- Orchestrate Manufacturing Task Workflows with Microsoft Agent Framework and Paperclip
- Coordinate Supply Chain Agents with LangGraph and Google ADK
- Build Autonomous Factory Inspection Agents with CrewAI and PydanticAI
- Automate Logistics Networks with smolagents and LangGraph
- Scale Procurement Task Distribution with Semantic Kernel and Prefect
- Orchestrate Equipment Monitoring Agents with llama-agents and FastAPI
- Automate Inventory Management Agents with OpenAI Agents SDK and Prefect
- Coordinate Manufacturing Process Agents with AutoGen and Microsoft Agent 365
- Dispatch Quality Control Agents with smolagents and OpenAI Agents SDK
- Orchestrate Industrial Maintenance Agents with Microsoft Agent Framework and LangGraph
- Deploy Predictive Supply Chain Agents with AutoGen and FastAPI
- Build Multi-Agent Quality Inspection Workflows with CrewAI and Semantic Kernel
- Monitor Manufacturing Agent Performance with PydanticAI and Prefect
- Build Defect Detection Agent Networks with CrewAI and LangGraph
- Coordinate Assembly Verification Agents with AutoGen and PydanticAI
- Automate Production Reporting Agents with smolagents and Prefect
- Monitor Predictive Maintenance Agents with smolagents and Semantic Kernel
- Route Warehouse Decisions with LangGraph and OpenAI Agents SDK
- Orchestrate Cross-Framework Supply Agents with Google ADK and CrewAI
- Edge AI & Inference
- View All Edge AI & Inference
- Deploy Quantized Models to Factory Edge Devices with vLLM and ExecuTorch
- Optimize Automotive Inference Pipelines with TensorRT-LLM and ONNX Runtime
- Run Edge LLMs on IoT Devices with Ollama and llama.cpp
- Accelerate In-Vehicle AI with TensorRT Edge-LLM and Jetson T4000
- Deploy Quantized LLMs to Industrial Sensors with CTranslate2 and Triton
- Optimize Factory Vision Models with OpenVINO and ExecuTorch
- Optimize Edge LLM Serving with vLLM and NVIDIA Model-Optimizer
- Deploy Inference Pipelines with Triton Inference Server and NVIDIA Model-Optimizer
- Accelerate Sensor Analytics with ONNX Runtime and vLLM
- Deploy Edge LLMs for Factory Diagnostics with LiteRT-LM and Hugging Face Transformers
- Serve High-Throughput Factory LLMs with vLLM and BentoML
- Run Compact Vision-Language Models for Industrial Inspection with Ollama and Supervision
- Run Hybrid LLM and ML Pipelines on Edge Gateways with Ollama and ONNX Runtime
- Deploy Multimodal Factory Models for NVIDIA and ARM Targets with TensorRT-LLM and ExecuTorch
- Optimize Cross-Platform NLP Inference for Industrial Gateways with CTranslate2 and ONNX Runtime
- Deploy Factory LLMs to Intel NPU with llama.cpp and OpenVINO
- Run Multi-Model Inference Pipelines on Factory Edge with ExecuTorch and ONNX Runtime
- Serve Lightweight Vision Models on Industrial Cameras with TFLite and Triton Inference Server
- Predictive Analytics & Forecasting
- View All Predictive Analytics & Forecasting
- Forecast Equipment Maintenance Windows with TimesFM and XGBoost
- Predict Demand Spikes with statsforecast and scikit-learn
- Detect Manufacturing Anomalies with NeuralForecast and PyTorch
- Build Real-Time Production Forecasts with TimeGPT-1 and Darts
- Optimize Supply Chain Forecasts with Darts and Amazon Forecast SDK
- Scale Industrial Forecasting with GluonTS and scikit-learn Ensemble Methods
- Build Multi-Step Ahead Forecasts with PyTorch Forecasting and statsmodels
- Forecast Energy Grid Load with Moirai and Prophet
- Predict Spare Parts Demand with Chronos-2 and XGBoost
- Estimate Equipment Remaining Useful Life with Moirai and scikit-learn
- Detect Equipment Anomalies in Real Time with NeuralForecast and XGBoost
- Forecast Logistics Demand Patterns with statsforecast and Prophet
- Model Factory Production Output with PyTorch Forecasting and GluonTS
- Scale Industrial Demand Forecasting to the Cloud with NeuralForecast and Amazon Forecast SDK
- Forecast Equipment Failure Windows with Chronos-2 and Prophet
- Build Interpretable Production Yield Forecasts with Prophet and scikit-learn
- AI Infrastructure & DevOps
- View All AI Infrastructure & DevOps
- Orchestrate Distributed AI Workloads with Ray and Kubernetes Python Client
- Deploy Model Inference with Triton Server and ArgoCD
- Monitor AI Model Health with Prometheus Client and BentoML
- Serve Production Models at Scale with Seldon Core and Prometheus Client
- Orchestrate Multi-Cloud AI Workloads with SkyPilot and Docker SDK
- Implement AI-Driven Infrastructure Observability with Prometheus Client and KServe
- Package Industrial ML Services with BentoML and Docker SDK
- Autoscale LLM Inference Endpoints with vLLM and KServe
- Trace Inference Pipeline Latency with vLLM and OpenTelemetry
- Distribute Model Training Across Clouds with Ray and SkyPilot
- Scale Distributed AI Training Across Clusters with Ray and ArgoCD
- Manage Industrial Model Fleets with Kubernetes Python Client and Seldon Core
- Automate Model Rollouts with ArgoCD and BentoML
- Trace and Monitor Industrial LLM Inference with OpenTelemetry and KServe
- Implement Canary Model Deployments for Industrial AI with Seldon Core and ArgoCD
- Automate Factory AI Container Lifecycle with Docker SDK and Kubernetes Python Client
- Data Engineering & Streaming
- View All Data Engineering & Streaming
- Ingest Manufacturing Sensor Streams into a Data Lakehouse with Redpanda and PyIceberg
- Detect Industrial Equipment Anomalies in Real Time with Flink Agents and Apache Kafka
- Process IIoT Sensor Streams at the Edge with Bytewax and Polars
- Stream IoT Sensor Data into Lakehouse Tables with Kafka and Flink CDC
- Analyze Edge Sensor Data with DuckDB and Polars
- Build Manufacturing Data Pipelines with dbt and Apache Spark
- Enrich Industrial Sensor Streams with PyFlink and Hugging Face Transformers
- Build Real-Time Lakehouse Analytics for Manufacturing with DataFusion and PyIceberg
- Write Factory CDC Streams to Delta Lake with Bytewax and Delta-rs
- Process Real-Time Assembly Line Metrics with PyFlink and Polars
- Transform Manufacturing Analytics Pipelines with dbt and DuckDB
- Stream Factory Sensor Events to Delta Lake with Apache Kafka and delta-rs
- Document Intelligence & NLP
- View All Document Intelligence & NLP
- Extract Structured Fields from Manufacturing Invoices with PaddleOCR and Docling
- Build a Technical Specification RAG Pipeline with Docling and Haystack
- Classify and Extract Compliance Documents with Unstructured and spaCy
- Extract Technical Drawings from PDF Specs with PyMuPDF and Supervision
- Classify Manufacturing Regulations with LayoutParser and Haystack
- Process Warranty Claims with Marker and spaCy NER
- Extract Structured Data from Engineering Diagrams with dots.mocr and spaCy
- Parse Complex Technical Documents at Scale with GLM-OCR and Docling
- Process Industrial PDF Archives with Mistral OCR and Haystack
- Convert Equipment Manuals to Searchable Knowledge Bases with Granite-Docling and LlamaIndex
- Parse and Index Equipment Maintenance Reports with Tesseract and Docling
- Process Unstructured Factory Documents into Search Pipelines with Unstructured and Haystack
- Extract Structured Data from Engineering Drawings with DocTR and LlamaIndex
- Extract Compliance Data from Industrial Forms with Azure Document Intelligence SDK and spaCy
- Company
Contact Now
Transforming Weld Quality Assessment with AI1-2
Client: Sulzer
<p class="d-md-none d-lg-block pb-3 mb-2 mb-md-3">Sulzer, a leader in industrial engineering, faced the challenge of ensuring the quality and integrity of welded components in their manufacturing processes. Our AI-based weld detection platform revolutionized their quality control. By analyzing images and data, it identifies welding defects with remarkable accuracy, enabling Sulzer to maintain the highest standards of product quality and safety.</p>
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In partnership with Sulzer, we embarked on a transformative journey to enhance weld quality assessment within their manufacturing processes. Leveraging cutting-edge AI technology, we addressed their challenges and revolutionized their approach to quality control.
Sulzer, a Switzerland-based leading player in the manufacturing industry, faced a critical challenge in maintaining consistent weld quality across their production lines. Manual inspection processes were time-consuming, error-prone, and hindered efficiency.
The Challenge
- Inconsistent weld quality assessments
- Time-consuming manual inspection processes
- Frequent errors in quality control
- Production line bottlenecks due to inspection delays
Our Solution
- Developed a custom AI model for weld quality assessment
- Integrated AI-powered inspection seamlessly into existing processes
- Real-time, automated quality assessment improved accuracy
- Reduced inspection time, eliminating production bottlenecks
Key Features
Let's Transform Your Manufacturing Now
Hit the button below to understand how you could integrate AI into your current manufacturing processes
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