Menu
- AI Readiness
- Industries
- Services
- All Services
- Conversational & Generative AI Systems
- Document Intelligence & Automation
- Predictive Intelligence & Forecasting
- Computer Vision & Edge AI Systems
- AI Product Development: From Idea to MVP/POC
- MLOps Cloud Engineering
- Data Mining & Warehousing
- Advanced Analytical Systems
- LLM Based Outreach Systems
- End-to-End AI System
- Technologies
- All Technologies
- 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
Atomic AI
<p class="pb-3 mb-2 mb-md-3">We jokingly call Atomic AI, the easiest AI. This is a no-code platform that helps you build robust AI models by using a drag-and-drop style interface. You just drop your data and let us worry about everything else</p><ul class="list-unstyled mb-4 mb-md-5"> <li class="d-flex mb-2"> <i class="bx bx-check text-primary lead me-2"></i> No-Code end-to-end model training platform </li> <li class="d-flex mb-2"> <i class="bx bx-check text-primary lead me-2"></i> Perform various ML training experiments </li> <li class="d-flex"> <i class="bx bx-check text-primary lead me-2"></i> Headless training and quick inferencing </li> </ul>
Welcome to Atomic AI, your gateway to effortless image classification. Our user-friendly, no-code platform simplifies the process of training and deploying image classification models, empowering users of all backgrounds to harness the power of artificial intelligence effortlessly.
The Challenge
- Overcoming the complexity of traditional machine learning
- Lack of accessibility for non-technical users
- Time-consuming model development and training
- Difficulty in achieving accurate image classifications
- Limited options for customization
- Steep learning curve for newcomers
- Expensive and resource-intensive AI development
Our Solution
- No-code platform for effortless model creation
- User-friendly interface requiring no coding skills
- Rapid model development and real-time feedback
- Improved accuracy in image classification tasks
- Highly customizable models for diverse applications
- Reduced learning curve, making AI accessible
- Cost-effective and resource-efficient solution
Features
test
Gallery
