You will get a custom OCR pipeline to extract text from documents, images, and PDFs
Project details
Need to extract data from scanned PDFs, invoices, receipts, ID cards, forms, or handwritten documents? I build end-to-end OCR pipelines using Tesseract, PaddleOCR, EasyOCR, and vision LLMs (GPT-4o, Claude) for intelligent extraction.
I pre-process images for better accuracy (deskew, denoise, binarization), extract structured fields (tables, key-value pairs, checkboxes), and return clean JSON/CSV/Excel output — ready for your database or downstream workflow.
Common use cases: invoice processing · medical records · bank statements · KYC document extraction · form digitisation · bulk document automation · ERP/CRM integration.
I pre-process images for better accuracy (deskew, denoise, binarization), extract structured fields (tables, key-value pairs, checkboxes), and return clean JSON/CSV/Excel output — ready for your database or downstream workflow.
Common use cases: invoice processing · medical records · bank statements · KYC document extraction · form digitisation · bulk document automation · ERP/CRM integration.
Programming Languages
JavaScript, Python, TypeScriptWhat's included
| Service Tiers |
Starter
$80
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 2 | 4 | 4 |
Number of Pages | 100 | 500 | 10000 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | |||
Source Code |
Frequently asked questions
About Sayed Ali
AI Engineer | Agentic AI | n8n, RAG, LLMs, MCP | AI Automation
Peshawar, Pakistan - 5:21 pm local time
As an AI Engineer, I design, build, and deploy end-to-end AI systems — agentic workflows, RAG applications, LLM integrations, and n8n automations that actually go live and deliver measurable results. My work sits at the intersection of solid architecture and real business outcomes. I don't hand you a prototype; I hand you a working system.
I have gone deep into how these systems actually work — from the mathematics of machine learning to the architecture of production AI applications. That depth matters for you because anyone can connect an API and call it AI. Not everyone understands why the retrieval is failing, why the agent is looping, why the fine-tuned model is degrading, or why the automation breaks at step 7. I do. And I fix it.
🔧 What I build and how I think about each one:
🤖 Agentic AI Systems
Agents are not chatbots with extra steps. A real agent perceives a situation, decides what to do, uses tools, and acts — without someone holding its hand through every decision. I build agents using LangChain, LangGraph, and CrewAI that handle real business tasks: research, data extraction, customer handling, multi-step decision workflows. I design the memory, the tool use, the guardrails, and the fallback logic — because an agent without guardrails is just an expensive way to make mistakes at scale.
🧠 RAG Applications
Most businesses are sitting on data they cannot use. PDFs, internal documents, product manuals, support histories, databases — none of it accessible to their team or their customers in real time. RAG changes that. I build retrieval systems that chunk, embed, index, and retrieve your data with precision — using Pinecone, ChromaDB, or Weaviate depending on your scale and budget. The result is an AI that answers from your data, not from hallucination.
⚡ n8n Automations
n8n is the most powerful automation tool most businesses have never heard of. I build multi-step workflows that connect your CRM, your email, your database, your APIs, and your AI layer into one coherent system that runs without anyone touching it. Lead capture, invoice triggers, data sync, scheduled reporting, Slack notifications, WhatsApp follow-ups — if it is repetitive and it costs your team time, it should be a workflow, not a task.
🔗 LLM Integration
I integrate OpenAI, Anthropic Claude, Google Gemini, and open-source models via HuggingFace into existing products and workflows. This includes prompt engineering for consistent outputs, fine-tuning on domain-specific data, structured output design, and embedding AI into applications in a way that feels natural rather than bolted on.
🌐 Full-Stack Web Applications with AI
The AI layer is only as good as the product around it. I build complete web applications — React and Next.js on the frontend, Node.js and Express or FastAPI on the backend, MongoDB or PostgreSQL for the database, and REST APIs connecting everything. Then I add the AI on top. One person. Full ownership. No handoff problems between a frontend developer who does not understand the model and a backend developer who does not understand the UI.
💡 What makes my approach different:
I have spent years studying how businesses actually operate — not just technology, but the operations, the sales, the logistics, the points where things break under pressure. When I build an automation for you, I am not guessing at your operations. I already understand them.
I also study game theory and philosophy alongside engineering. Game theory shapes how I design systems that work with human behavior, not against it. Philosophy sharpens how I define problems before I start solving them. Most engineers jump to the solution. I make sure we are solving the right problem first.
🛠️ Technologies I work with:
Python · JavaScript · React · Next.js · Node.js · Express · FastAPI · MongoDB · PostgreSQL · n8n · LangChain · LangGraph · CrewAI · OpenAI API · Anthropic Claude API · HuggingFace · Pinecone · ChromaDB · Weaviate · Docker · REST APIs · Streamlit · Gradio · Prompt Engineering · Fine-Tuning · Vector Embeddings · RAG · AI Agents
📩 If you have a business problem that AI or automation could solve — or if you are not sure whether it can — send me a message. I will give you an honest answer within hours, and if I am not the right fit, I will tell you that too.
Steps for completing your project
After purchasing the project, send requirements so Sayed Ali can start the project.
Delivery time starts when Sayed Ali receives requirements from you.
Sayed Ali works on your project following the steps below.
Revisions may occur after the delivery date.
Analyzing the full problem and available resources
Brainstoring and designing a full OCR based on available resources