You will get Local OCR and document extraction — no cloud API, no per-page fees
Project details
Cloud OCR works well until you read the terms, or until someone asks where the invoices went. If your documents contain personal data, financial detail or anything contractual, uploading them to a third party may not be a decision you're allowed to make.
Vision-language models like Qwen2-VL now run on a single consumer GPU and handle far more than classic OCR. They read scanned documents, handwriting, forms and messy layouts, and return structured data directly rather than a wall of text you then have to parse.
I deploy one on your hardware and build the extraction pipeline around your document types — not a generic demo. You define the fields, I build the schema and tune extraction until it's accurate on your own samples, then hand over a batch script and a measured accuracy report.
No per-page fees, no upload limits, no internet connection needed once it runs.
If your documents are clean, uniform and non-sensitive, a cloud OCR API is cheaper and simpler and I'll say so. This is for the case where they aren't, or where volume makes per-page pricing painful.
Vision-language models like Qwen2-VL now run on a single consumer GPU and handle far more than classic OCR. They read scanned documents, handwriting, forms and messy layouts, and return structured data directly rather than a wall of text you then have to parse.
I deploy one on your hardware and build the extraction pipeline around your document types — not a generic demo. You define the fields, I build the schema and tune extraction until it's accurate on your own samples, then hand over a batch script and a measured accuracy report.
No per-page fees, no upload limits, no internet connection needed once it runs.
If your documents are clean, uniform and non-sensitive, a cloud OCR API is cheaper and simpler and I'll say so. This is for the case where they aren't, or where volume makes per-page pricing painful.
AI Development Type
Knowledge Representation, Model TuningAI Tools
NVIDIA AI Platform, OpenCV, PyTorchAI Development Language
PythonWhat's included $1,400
These options are included with the project scope.
$1,400
- Delivery Time 9 days
- Number of Revisions 2
- AI Model Integration
- Detailed Code Comments
- Model Documentation
- Source Code
Frequently asked questions
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Oliver S.
Mar 28, 2026
Build & Launch a Scalable AI SaaS MVP
After such a long journey working with Hamza, I found qualities of leadership, commitment, focus, understanding, taking care of his clients above all and honesty in Hamza. A beautiful work-relation. Would recommend him 100% to everyone looking forward to acquire IT services.
OS
Oliver S.
Aug 29, 2025
AI developer Vision Movie Hourly
Hamza Bilal is an excellent AI engineer and full stack developer. He combines strong technical expertise with a clear understanding of modern SaaS and AI-driven solutions. His ability to deliver reliable, scalable applications while also integrating advanced AI features makes him stand out. I’m very happy with his work and highly satisfied with his professionalism and results.
AM
Abdul M.
Jun 21, 2025
Fullstack Developer Python, FastAPI, Nextjs, Reactjs for Web Application from scratch
OS
Oliver S.
Mar 10, 2025
Update Last Screen in App (ASP.NET Core + NEXTJS)
Either you are unlucky or lucky with your freelancer. In this case we were lucky. Mr. Hamaz has mastered all of the skills mentioned very well. I recommend Mr. Hamaz. It is very important to him that everyone involved in the project is satisfied. He works in a very structured way. I recommend Mr. Hamaz with a clear conscience.
OS
Oliver S.
Dec 15, 2024
VisionMovieCh Hourly Contract
Hamaz Bilal is an excellent software engineer. Working with him is very pleasant and balanced. He works at a European level and is always available.
He had a positive influence on the development of the application. He presented good solutions. At the same time, he was not disappointed or negative when we did not pursue his approaches.
We would like to take this opportunity to say a big thank you to Hamas Bilal. We have already asked him whether he still has capacity for our new project in 2025. We hope for a positive response.
We highly recommend him. CEO O. Schwitter, Zurich
He had a positive influence on the development of the application. He presented good solutions. At the same time, he was not disappointed or negative when we did not pursue his approaches.
We would like to take this opportunity to say a big thank you to Hamas Bilal. We have already asked him whether he still has capacity for our new project in 2025. We hope for a positive response.
We highly recommend him. CEO O. Schwitter, Zurich
About Muhammad Hamza
AI Engineer | On-Prem LLM, RAG & Private GPU Deployment | Full-Stack
100%
Job Success
Lahore City, Pakistan - 2:11 pm local time
That's the difference between calling an API and running the model yourself. I do both — RAG pipelines, chatbots and voice agents on GPT, Gemini and Claude, plus private models running on your own hardware. Running models is the part most developers outsource.
For a US healthcare provider I built a system that turns raw inspection and testing findings into finished regulatory narrative documents — the kind that go on file with a federal agency, where wording and citation structure matter — using Mistral and Llama hosted entirely on their own hardware behind an ASP.NET Core backend. Nothing left their network. For a second US healthcare team I built a production patient-facing RAG chatbot over a vector database that books appointments, takes payments, discusses symptoms and routes patients to the right specialist. For a European university I replaced brittle spreadsheet workflows with a Next.js and FastAPI application for managing coursework between students and instructors.
I don't recommend a model I haven't run. I benchmark in phases, isolating one variable at a time — guidance, steps, sequence length, memory mode — then lock the winning configuration and ship it as a reproducible script. I've done this across four modalities on a single 24GB GPU: image (FLUX.1-dev and schnell, SDXL, Qwen-Image), video (Wan 2.1 and 2.2, SkyReels), music (ACE-Step) and voice
(Chatterbox, benchmarked across seven languages). Also open-weight LLMs — Devstral alongside the Mistral, Llama, Qwen and GLM families — and Qwen2-VL for local OCR where documents can't go to a cloud API. The same pipelines run on RunPod and Vast.ai.
That process is how you learn things documentation won't tell you. For reference-to-video, resolution turned out to be the stability lever, not prompt engineering — six controlled tests to establish it, and it saved weeks of tuning the wrong variable.
That work became Motivia, a multi-modal generative media SaaS now in production, producing complete up to 60 second videos with voiceover and original music. Seven generation types, async GPU inference behind a Redis queue, server-side video compositing with FFmpeg, and a multi-GPU orchestration layer that guarantees visual consistency across every scene in a video.
I build local AI agents too — a voice assistant running Whisper large-v3 and speaker identification on-GPU with no cloud audio processing at all, a coding agent panel, and a memory system. Tauri desktop apps with Rust backends, a Python sidecar holding the speech models on the GPU, WebSocket between them.
Across 12 Upwork contracts I hold a 100% Job Success Score and five stars on every job that was rated.
If a hosted API is the right answer for your problem, I'll say so and talk you out of paying me to build infrastructure you don't need. I'd rather lose the contract than ship you something expensive that a $40/month subscription would have handled.
But when your data can't leave the building, or per-token pricing breaks your unit economics, that's the work I do.
Tell me what you're building and what your constraints are.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Hamza can start the project.
Delivery time starts when Muhammad Hamza receives requirements from you.
Muhammad Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
Sample review and schema definition
I run your samples through candidate models to see what's realistically achievable, and we agree the exact output schema. If your documents are too degraded for reliable extraction, I tell you here.
Deploy the model locally
The vision-language model is installed on your machine, tuned for your VRAM, and verified running offline. No cloud service is involved at any point, including during this testing.