You will get AI Tool That Sorts 10,000 Page PDFs in Minutes
Project details
You will get a powerful AI system that automatically reads, sorts, and extracts structured data from large PDF documents in minutes. I built this solution end to end, including frontend interface and backend AI pipeline, so you get a complete production ready system, not just a script.
Whether you are handling legal files, medical records, research papers, or compliance documents, this tool detects sections, extracts custom fields, and exports clean structured data in JSON, CSV, or database format.
What sets me apart is real world AI engineering experience. I focus on performance, accuracy, and scalability. You receive clean architecture, optimized processing for large documents, and optional API or cloud deployment support.
This is not a basic PDF reader. This is an AI powered document automation engine.
Whether you are handling legal files, medical records, research papers, or compliance documents, this tool detects sections, extracts custom fields, and exports clean structured data in JSON, CSV, or database format.
What sets me apart is real world AI engineering experience. I focus on performance, accuracy, and scalability. You receive clean architecture, optimized processing for large documents, and optional API or cloud deployment support.
This is not a basic PDF reader. This is an AI powered document automation engine.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Deeplearning4j, MLflow, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$150
|
Standard
$500
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 10 days | 15 days |
Number of Revisions | 1 | 3 | |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | - | ||
Taxonomy | - | - |
Frequently asked questions
About Sami
Full Stack AI Engineer | RAG Systems & Document Automation
Rawalpindi, Pakistan - 2:09 am local time
Transforming unstructured data into enterprise-grade AI solutions.
I architect, build, and deploy production-ready AI systems across the complete ML pipeline: from data engineering and model optimization to MLOps infrastructure and full-stack deployment. Specializing in document intelligence automation, retrieval-augmented generation (RAG), and multimodal LLM applications.
CORE EXPERTISE:
🔹 End-to-End Document Intelligence & Automation
→ Advanced OCR with transformer-based models (TrOCR, PaddleOCR, Azure Vision API)
→ Intelligent PDF extraction using vision transformers and semantic segmentation
→ Layout-aware document parsing with graph neural networks (GNNs) and attention mechanisms
→ Batch processing pipelines with asynchronous job orchestration and fault tolerance
→ Handwriting recognition and synthetic data augmentation for improved generalization
🔹 Large Language Models & Retrieval-Augmented Generation (RAG)
→ Production RAG systems with dense vector retrieval and semantic reranking
→ Fine-tuning LLMs (LoRA, QLoRA, full fine-tuning) for domain-specific tasks
→ Multi-modal LLM applications integrating vision-language models (CLIP, LLaVA, GPT-4V)
→ Prompt engineering and chain-of-thought reasoning for complex reasoning tasks
→ Vector database optimisation (FAISS, Qdrant, Pinecone, Milvus) with approximate nearest neighbor search
→ Context window optimisation and token-efficient inference techniques
🔹 Full-Stack Backend Architecture & API Development
→ RESTful API design with FastAPI, async patterns, and performance optimization
→ Microservices architecture with containerization (Docker, Kubernetes)
→ Authentication, authorization, and security hardening (OAuth2, JWT, encryption)
→ Database design: relational (PostgreSQL), NoSQL (MongoDB), vector DBs, graph DBs
→ Real-time data streaming and event-driven architectures (Apache Kafka, Redis)
→ API gateway patterns, rate limiting, and traffic management
🔹 Machine Learning Operations & Production Systems
→ ML pipeline orchestration (Airflow, Prefect) with data lineage tracking
→ Model registry and versioning (MLflow, Weights & Biases, DVC)
→ A/B testing frameworks and experiment management
→ Continuous integration/continuous deployment (CI/CD) for ML models
→ Model monitoring, drift detection, and automated retraining pipelines
→ Feature engineering, feature stores, and data quality management
🔹 Advanced Deep Learning & Computer Vision
→ Object detection (YOLO, Faster R-CNN, EfficientDet) with real-time inference optimization
→ Semantic segmentation and instance segmentation (Mask R-CNN, DeepLabV3)
→ Image classification with transfer learning and domain adaptation techniques
→ Real-time video analytics pipelines (NVIDIA DeepStream, OpenCV)
→ Model compression: quantization, pruning, knowledge distillation for edge deployment
→ TensorRT optimization for GPU inference acceleration (40%+ speed improvements)
🔹 Generative AI & Creative Workflows
→ Diffusion models and latent diffusion implementation (Stable Diffusion, SDXL)
→ Workflow automation (ComfyUI) for image generation and video synthesis
→ Text-to-image and image-to-image generation with fine-tuned models
→ Multimodal generation pipelines and conditional image synthesis
🔹 Cloud Infrastructure & Deployment
→ AWS (EC2, S3, Lambda, SageMaker, RDS), Azure (Blob Storage, Cognitive Services), GCP
→ Serverless deployments (AWS Lambda, Azure Functions, RunPod Serverless)
→ GPU cluster management and distributed training (PyTorch DDP, NVIDIA NCCL)
→ Infrastructure as Code (Terraform, CloudFormation) and GitOps workflows
RESULTS & IMPACT:
✅ Production Deployments: 10+ enterprise AI systems deployed to production
✅ Performance Optimization: 40%+ cost reduction through TensorRT quantization and inference optimization
✅ Accuracy Metrics: 99%+ precision on OCR tasks through transformer-based architectures
✅ Scalability: Architected systems processing 100k+ documents monthly
✅ Time Savings: Clients report 80%+ reduction in manual data entry workflows
✅ MLOps Excellence: Zero-downtime deployments with automated monitoring and alerting
TECH STACK (Production-Grade):
AI/ML Frameworks: PyTorch, TensorFlow, JAX, HuggingFace Transformers, LangChain, LlamaIndex
NLP: Sentence-Transformers, spaCy, NLTK, Prompt Engineering, LLM fine-tuning (LoRA/QLoRA)
Vision: OpenCV, Pillow, torchvision, YOLO, Faster R-CNN, SAM (Segment Anything Model)
OCR: Tesseract, EasyOCR, PaddleOCR, TrOCR, Azure Vision, Google Vision API
Vector DBs: FAISS, Qdrant, Pinecone, Milvus, Weaviate, ChromaDB
Backend: FastAPI, Flask, Django, async/await patterns, WebSockets
Databases: PostgreSQL, MongoDB, Redis, ClickHouse, graph DBs (Neo4j)
MLOps: MLflow, Airflow, Prefect, DVC, Weights & Biases, GitHub Actions
Deployment: Docker, Kubernetes, AWS, Azure, GCP, RunPod, Railway, Render
Generative AI: ComfyUI, Stable Diffusion, ControlNet, CLIP, DALL-E
Steps for completing your project
After purchasing the project, send requirements so Sami can start the project.
Delivery time starts when Sami receives requirements from you.
Sami works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis
Client shares PDF samples, total page count, extraction fields, and output format.
AI Processing & Structuring
Documents are processed, sections detected, and required data extracted.