You will get a custom AI tool to extract data from PDFs, invoices, and scanned documents
Project details
This is a custom-built AI document extraction tool. You hand it invoices, receipts, IDs, contracts, or forms, and it hands you back clean structured data, ready to use.
Not a no-code wrapper. A real engineering pipeline you own, source code on day one.
How it works:
Ingest: PDF, image, or phone scan via FastAPI endpoint.
OCR: routed per doc type (Textract, Google Document AI, Tesseract).
LLM validation: GPT-4 Vision or Claude pulls the fields you asked for.
Confidence and rules: every field scored, low-confidence ones flagged for review.
Output: JSON file by default, or pushed straight into a tool your team already uses.
Tiers:
Starter, $179: One doc type. Clean JSON output. Source code. Wire it into your own system on your end.
Standard, $549 (most popular): Up to 3 doc types. Confidence scoring plus validation rules. Extracted data flows directly into one tool such as Google Sheets, Airtable, a webhook, or a database table.
Advanced, $1,599: Drop a mixed pile in, a custom classifier sorts it before extraction. Includes a review screen for low-confidence fields and push to one CRM/ERP (QuickBooks, Xero, Salesforce, NetSuite). Deployed to your cloud.
Not a no-code wrapper. A real engineering pipeline you own, source code on day one.
How it works:
Ingest: PDF, image, or phone scan via FastAPI endpoint.
OCR: routed per doc type (Textract, Google Document AI, Tesseract).
LLM validation: GPT-4 Vision or Claude pulls the fields you asked for.
Confidence and rules: every field scored, low-confidence ones flagged for review.
Output: JSON file by default, or pushed straight into a tool your team already uses.
Tiers:
Starter, $179: One doc type. Clean JSON output. Source code. Wire it into your own system on your end.
Standard, $549 (most popular): Up to 3 doc types. Confidence scoring plus validation rules. Extracted data flows directly into one tool such as Google Sheets, Airtable, a webhook, or a database table.
Advanced, $1,599: Drop a mixed pile in, a custom classifier sorts it before extraction. Includes a review screen for low-confidence fields and push to one CRM/ERP (QuickBooks, Xero, Salesforce, NetSuite). Deployed to your cloud.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
Amazon SageMaker, OpenCV, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$179
|
Standard
$279
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 20 days |
Number of Revisions | 2 | 5 | 5 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $100
Additional Revision
+$25
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Marzieh B.
Jun 6, 2026
Prepare for ML interviews
MK
Masseera K.
May 25, 2026
restaurant order booking Voice agent
Everything was done very professionally and the end result was perfect. Will be happy to work again with him. Good Work Noman
About Nouman
AI Engineer | RAG, Voice Agents & Vision Systems That Ship
100%
Job Success
Lahore, Pakistan - 7:48 pm local time
You need AI that ships, stays on budget, and becomes a reliable part of your product.
I’m an AI Engineer with 5+ years of experience building production-ready AI systems across RAG, voice agents, computer vision, document intelligence, and recommendation systems. I build AI as accountable, measurable software — not magic you hope behaves.
I have worked as Lead AI Engineer for Bookme.pk and Bookme.sa, where I shipped recommendation systems, document extractors, and intelligent travel-guide agents used by real customers at scale.
Recent AI Projects
CallFlow — Real-Time Voice AI Agents
Built a production-style voice AI demo platform with 3 live business agents:
• AI Receptionist for appointment booking and rescheduling
• AI Lead Qualifier for real estate lead qualification
• AI Customer Support agent for order inquiries, returns, and product questions
The system is designed around real-time voice interaction with sub-second response behavior, browser-based calling, and integration-ready workflows for CRMs, calendars, knowledge bases, APIs, Twilio, WhatsApp, and web channels.
OmniSight — AI Video Monitoring System
Built an AI-assisted surveillance and monitoring system where users can describe what they want to detect in plain English.
Example rules include:
• “Person in restricted area after 6pm”
• “Vehicle blocking the emergency exit”
• “Unattended object near entrance”
The system processes webcam/video input, evaluates frames with AI vision, detects rule matches, and returns alert-ready visual evidence. This is useful for security monitoring, safety compliance, retail operations, and industrial inspection workflows.
Quill — AI Writing Workspace
Built a Notion-style AI writing editor where AI is embedded directly into the writing flow instead of forcing users to switch tabs.
Key features include:
• AI autocomplete using inline “++” commands
• In-place rewriting and grammar improvement
• Shorten, expand, and improve selected text
• 12+ editor block types
• Markdown support
• Rich content embeds
• Dark mode
This project demonstrates how I build AI features that feel native inside a product rather than bolted on as a chatbot.
Tickline — Financial Filing RAG Assistant
Built a financial RAG system for question-answering over 10-K style filing data.
The assistant is grounded across 5 major filing sections, including:
• Business Overview
• Risk Factors
• MD&A
• Financial Statements
• Segment Reporting
It answers financial questions with cited source sections and exact supporting excerpts, reducing hallucinations and making the output easier to verify.
Bookme.sa Intelligent Travel Guide
Built an intelligent travel-guide agent grounded in real inventory rather than generic AI knowledge. The goal was to help users get accurate, useful recommendations while keeping inference cost predictable and scalable.
What I Can Help You Build
• RAG systems and knowledge assistants
• AI chatbots and agentic workflows
• Voice AI agents and real-time conversational interfaces
• AI document editors and writing tools
• Computer vision and video monitoring systems
• OCR and document extraction pipelines
• Financial, legal, healthcare, or internal-document Q&A systems
• Recommendation engines
• AI prototypes that can actually become production features
How I Work
I don’t start by drowning you in meetings or vague AI strategy documents.
I work in short, practical sprints:
1. Understand the use case and success criteria
2. Build a working prototype quickly
3. Test it against real examples
4. Measure quality, latency, and cost
5. Improve until it is ready for production
You see something real early, give feedback on an actual product, and we refine from there.
Why Clients Hire Me
• 5+ years of hands-on AI/ML engineering experience
• Production experience, not just notebooks and demos
• Strong focus on cost control and scalable architecture
• Experience with RAG, voice AI, computer vision, OCR, and recommendation systems
• Ability to turn vague AI ideas into clear, testable product features
• Comfortable building end-to-end: backend, AI pipeline, APIs, deployment, and evaluation
Tech Stack
LLMs & AI Frameworks
OpenAI GPT, Anthropic Claude, Google Gemini, Hugging Face, LangChain, LangGraph, LlamaIndex
RAG & Vector Search
Pinecone, Weaviate, Qdrant, Chroma, Cohere Rerank, RAGAS, LangSmith, Langfuse
Voice AI
OpenAI Realtime API, LiveKit, Vapi, Pipecat, Deepgram, ElevenLabs, Twilio
Computer Vision & OCR
OpenCV, YOLO, PyTorch, Roboflow, SAM 2, MediaPipe, ONNX Runtime, Tesseract, Cloud OCR, Vision-Language Models
Backend & Deployment
Python, FastAPI, Docker, AWS, Next.js, Vercel, Weights & Biases, MLflow, MCP
If you have an AI use case, send me a message. I’ll tell you honestly whether AI is the right fit, what I would prototype first, and how to avoid wasting budget on a system that looks good in a demo but fails in production.
Steps for completing your project
After purchasing the project, send requirements so Nouman can start the project.
Delivery time starts when Nouman receives requirements from you.
Nouman works on your project following the steps below.
Revisions may occur after the delivery date.
I confirm scope in 24 hours
I review your samples, confirm what's in scope, and flag anything unusual. You'll get a clear list of which paid services we need API keys for, with rough cost per document so there are no surprises before any code gets written.
I build against your docs
OCR routing, LLM validation, confidence scoring, and your chosen output destination. Built and tuned against your real samples, not generic test data, so accuracy reflects what you actually deal with day to day in production.