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- LLM Engineering & Fine-Tuning
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- 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
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- 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
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- 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
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- Train Robotic Manipulation Policies with LeRobot and Isaac Lab
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- 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
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- 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
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- Build Industrial Equipment Twins with Siemens Composer and MLflow
- Monitor Assembly Line Health with Evidently and YOLO26
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- Validate Manufacturing Data Pipelines with Great Expectations and DVC
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- Segment Industrial Defects with Florence-2 and Detectron2
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- Orchestrate Manufacturing Task Workflows with Microsoft Agent Framework and Paperclip
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- Deploy Quantized Models to Factory Edge Devices with vLLM and ExecuTorch
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- Predictive Analytics & Forecasting
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- Forecast Equipment Maintenance Windows with TimesFM and XGBoost
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- 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
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- Orchestrate Distributed AI Workloads with Ray and Kubernetes Python Client
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- Monitor AI Model Health with Prometheus Client and BentoML
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- 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
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Detecting Milk Adulteration with AI
Client: Amul
<p class="d-md-none d-lg-block pb-3 mb-2 mb-md-3">Amul, a household name in dairy products, was determined to combat milk adulteration. Our AI solution, specifically tailored for milk quality analysis, detects adulterants with precision. This technology safeguards the purity of Amul's dairy products, ensuring consumer trust and product integrity.</p>
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In an industry that demands the highest standards of quality and purity, Amul faced a formidable challenge in ensuring the integrity of their dairy products. Detecting milk adulteration posed a significant hurdle, one that required an innovative solution.
Enter our AI-powered technology, a transformative tool that with Amul their mission to combat milk adulteration. This case study explores how our AI solution became an invaluable asset, revolutionizing milk quality control processes and ensuring the delivery of pure, unadulterated dairy products to consumers.
The Challenge
- Inaccurate detection of milk adulterants
- Growing concerns about product safety and consumer trust
- Difficulty in maintaining consistent milk quality
- The need for a reliable and efficient quality control solution
Our Solution
- Developed a custom AI model for milk adulteration detection
- Integrated AI seamlessly into existing milk testing processes
- Achieved real-time, precise identification of adulterants
- Strengthened product quality and consumer confidence
Key Features
Let's Transform Your Quality Detection Process Now
Hit the button below to understand how you could integrate AI into your current qualitty detection process
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