You will get production RAG system that answers questions from your documents instantly
Project details
You will get a production RAG system that answers questions from your documents instantly
If your team spends hours searching through documents, contracts, reports, or manuals to find specific information, I can fix that.
I build RAG systems where anyone on your team types a plain-language question and gets a precise, sourced answer in seconds. Every response includes the exact document and section it came from, so your team can verify it instantly.
On a recent system, I built a 200-question evaluation benchmark before writing a single line of the pipeline, then used it to iterate until retrieval accuracy hit 94% across 50,000+ document chunks with sub-200ms response time.
I use LangChain, LlamaIndex, Pinecone, semantic chunking, and hybrid search. You get full source code, a deployed API or web interface, and a system your team can actually trust.
Send me a message with your document type and use case and I will tell you the right architecture.
If your team spends hours searching through documents, contracts, reports, or manuals to find specific information, I can fix that.
I build RAG systems where anyone on your team types a plain-language question and gets a precise, sourced answer in seconds. Every response includes the exact document and section it came from, so your team can verify it instantly.
On a recent system, I built a 200-question evaluation benchmark before writing a single line of the pipeline, then used it to iterate until retrieval accuracy hit 94% across 50,000+ document chunks with sub-200ms response time.
I use LangChain, LlamaIndex, Pinecone, semantic chunking, and hybrid search. You get full source code, a deployed API or web interface, and a system your team can actually trust.
Send me a message with your document type and use case and I will tell you the right architecture.
AI Algorithms
AlexNet, Autoencoder, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Transformer Model, Variational Autoencoder, YOLOAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, AI-Enhanced Medical Imaging, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Speech SynthesisAI Development Language
PythonAI Tools
Adobe Firefly, Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, Dolly, GPT-4, LaMDA, LLaMA, Midjourney AI, Naive Bayes Classifier, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$300
|
Standard
$800
|
Advanced
$2,300
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | |||
Model Deployment | |||
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Abdul Rehman
AI Agent Developer | RAG Systems, LLM App, Voice AI | Agentic Workflow
Lahore Cantt, Pakistan - 4:52 pm local time
and it keeps breaking in the real world, that is the exact problem I solve.
I build AI systems for founders, product teams, and businesses that need AI to actually work in
production, not just in a demo.
Here is what that looks like in practice:
If you have a large library of documents, contracts, reports, or manuals and your team spends
hours searching through them, I can build a system that answers any question from that library
instantly and accurately.
If you have a repetitive workflow where someone reads an input, makes a decision, and takes
an action, I can automate that with an AI agent that does it faster, consistently, without human
error.
If you want to add a voice assistant or an intelligent chatbot to your product that actually
understands your business context and can take actions (not just answer FAQs), I build those
too.
WHAT I HAVE SHIPPED
A document intelligence system that answers questions across 10,000+ documents in under 2
seconds, with 94% accuracy, for a client whose team was losing hours every week to manual
search.
A voice AI assistant with a talking avatar that responds naturally, handles back-and-forth
conversation, and is used in a real product today.
An AI agent that manages customer support automatically, resolving 85% of incoming tickets
without any human involvement, and routing the remaining 15% to the right team member.
A financial analytics platform where business owners can ask plain-language questions about
their data and get clear, visual answers instead of digging through spreadsheets.
WHO I WORK WITH
SaaS founders who want to add AI features that actually work and retain users.
Operations teams who want to cut the manual, repetitive work their people do every day.
Product teams who have tried AI tools and found they break at the edges.
Startups launching AI-powered products and needing someone who has done it before.
HOW IT WORKS
You tell me your problem in plain language. I ask a few questions to understand your workflow,
your data, and what success looks like. Then I scope it clearly and build it in iterations so you
can see progress before the final delivery.
No black boxes. No surprises. Just a working system at the end.
If that sounds like what you need, send me a message with your use case and I'll outline the best approach, expected timeline, and next steps.
Steps for completing your project
After purchasing the project, send requirements so Abdul Rehman can start the project.
Delivery time starts when Abdul Rehman receives requirements from you.
Abdul Rehman works on your project following the steps below.
Revisions may occur after the delivery date.
View the documents and then create RAG Architecture based on it