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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
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Industrial Automation & Robotics
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- Train Robotic Manipulation Policies with LeRobot and Isaac Lab
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Digital Twins & MLOps
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- Build Industrial Equipment Twins with Siemens Composer and MLflow
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Computer Vision & Perception
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- 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
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Multi-Agent Systems
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- Orchestrate Manufacturing Task Workflows with Microsoft Agent Framework and Paperclip
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Edge AI & Inference
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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
- 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
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- Scale Industrial Forecasting with GluonTS and scikit-learn Ensemble Methods
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- Forecast Energy Grid Load with Moirai and Prophet
- Predict Spare Parts Demand with Chronos-2 and XGBoost
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AI Infrastructure & DevOps
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- Orchestrate Distributed AI Workloads with Ray and Kubernetes Python Client
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- Autoscale LLM Inference Endpoints with vLLM and KServe
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- Package Industrial ML Services with BentoML and Docker SDK
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Data Engineering & Streaming
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- 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
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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
- Company
Document Intelligence & Automation
Transform unstructured documents into actionable intelligence with AI-powered automation and data enrichment. The Document Intelligence and Automation solutions offered by our company change the way businesses handle their information processing completely. Machine learning, natural language processing (NLP), and optical character recognition (OCR) are the main technologies behind the intelligent automation that we provide to our customers. This kind of automation can extract, classify, and confirm the data at a very high rate, which in turn reduces the manual effort and error in data handling.
Description
We create AI-based systems for document automation that have the capability of comprehending, extracting, and structuring the data from cumbersome and non-organized sources such as invoices, contracts, forms, and reports. By using deep learning-powered OCR, NLP-based entity recognition, and contextual enrichment together, we make it easy for the document-heavy workflows and thus improve data usability.
Our systems come with the support of enterprise-grade APIs as well as databases, thereby guaranteeing the large-scale document ecosystems' seamless ingestion, processing, and decision support.
Knowledge Base
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What is Document Intelligence & Automation?
Oct 30, 20254 mins read
Methodology
Document Ingestion & Classification
We characterize documents on an automated basis by employing semantic similarity and layout recognition through machine learning models.
Data Extraction & Recognition
Multi-format ingestion pipelines, which allow for the inclusion of PDFs, images, scanned forms, and emails, are a part of the integration process. AI-powered OCR engines like Tesseract, Google Vision AI, and Azure Form Recognizer pull out text and table data.
Contextual Data Enrichment
We augment the extracted information through the use of knowledge graphs, metadata mapping, and external data APIs; thus, the data is organized and contextualized for analytics or integration.
Validation & Quality Assurance
Automated validation pipelines check the extracted data against internal databases or business rules, applying confidence scoring and anomaly detection to guarantee the data's reliability.
Integration & Workflow Automation
The processed data is automatically pushed into ERP, CRM, or ECM systems via APIs, RPA bots, or event-driven pipelines—this allows for complete intelligent workflow automation from start to finish.
A few of our flagship implementations of production-ready systems
Check out the FAQs.1
Let’s Redefine How You Handle Information!
From ingestion to enrichment, our Document Intelligence solutions unlock the value hidden in unstructured data. We help businesses automate document-heavy workflows, enhance accuracy, and improve decision velocity with AI that learns and evolves continuously.
Invoices, receipts, contracts, reports, handwritten notes, and multi-page scanned documents— formats like PDF, JPEG, TIFF, and DOCX are all included.
Our extraction precision is about 95–99% and is constantly enhanced with adaptive learning and feedback loops.
Certainly. By utilizing deep learning-based handwriting recognition (HWR), we can extract and decipher cursive or printed handwriting in various languages.
The OCR merely records text, but our system comically captures context—through NLP and layout analysis, it extracts relations, categories, and meanings, etc.
All information is handled in encrypted, access-controlled environments that meet GDPR, SOC 2, and ISO 27001 standards.