You will get Private RAG AI System for Your Business Documents Self-Hosted, No Data Leag
Project details
Most RAG builds assume you can send your documents to OpenAI. I build the ones for teams that can't self-host retrieval and generation, so contracts, records, or case files never leave your infrastructure, with source-cited answers so your team can trust what it gets back.
If your documents are contracts, patient records, case files, or anything else you can't paste into ChatGPT, this is built for you.
I design and build Retrieval-Augmented Generation (RAG) systems that answer questions from your real documents — accurately, with source citations, and running entirely on infrastructure you control. No public API calls required for the sensitive parts of the pipeline.
What you get:
• A working RAG pipeline built on your actual documents (LangChain/LlamaIndex + a vector database)
• Answers grounded in your content with source citations, not generic AI guesses
• Fully self-hosted/local deployment (Ollama/vLLM) depending on your data sensitivity needs
• A short walkthrough so your team can actually use and maintain what's delivered.
I work in fixed, scoped sprints — you'll see it working on your real data before deciding on anything bigger.
If your documents are contracts, patient records, case files, or anything else you can't paste into ChatGPT, this is built for you.
I design and build Retrieval-Augmented Generation (RAG) systems that answer questions from your real documents — accurately, with source citations, and running entirely on infrastructure you control. No public API calls required for the sensitive parts of the pipeline.
What you get:
• A working RAG pipeline built on your actual documents (LangChain/LlamaIndex + a vector database)
• Answers grounded in your content with source citations, not generic AI guesses
• Fully self-hosted/local deployment (Ollama/vLLM) depending on your data sensitivity needs
• A short walkthrough so your team can actually use and maintain what's delivered.
I work in fixed, scoped sprints — you'll see it working on your real data before deciding on anything bigger.
Machine Learning Tools
ChatGPT, GPT-3, NLTK, Python, Python Scikit-LearnWhat's included
| Service Tiers |
Starter
$250
|
Standard
$750
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 3 | 6 | Unlimited |
Number of Model Variations | 9 | ||
Number of Scenarios | 2 | 6 | 12 |
Number of Graphs/Charts | 5 | 10 | 15 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $300
Additional Revision
+$50Frequently asked questions
About Sardar
AI Agent & RAG Developer | AI Chatbot Develop, LangChain, OpenAI, LLM
Abbottabad, Pakistan - 2:30 pm local time
I've spent the past two years building production RAG (Retrieval-Augmented Generation) systems and AI assistants for companies handling exactly this kind of sensitive data systems that run entirely inside a company's own infrastructure, self-hosted or fully offline, so nothing leaves your control while your team gets instant, source-cited answers.
What I bring:
- RAG pipelines built with LangChain/LlamaIndex + a vector database (Pinecone, Weaviate, or Chroma), tuned for accurate retrieval over contracts, records, and internal knowledge bases not just keyword search
- Self-hosted and fine-tuned open-source models (Llama 3.1, Qwen3, Mistral) deployed via Ollama/vLLM zero external API calls, so it holds up under a compliance review
- AI agents and workflow automation (n8n/CrewAI) that connect retrieval to real actions, not just Q&A
- Every system I build gets tested against questions it shouldn't be able to answer before handoff if it doesn't know, it says so instead of guessing
I work in short, fixed-scope sprints a working pilot over your first batch of real documents in 5–10 days, so you see real answers on real data before committing to anything bigger.
Send me a sample of your documents or the workflow you want automated. I'll tell you within 24 hours whether RAG is the right fit and exactly what a first pilot looks like.
I work as a freelance RAG and Machine Learning developer, delivering AI-driven solutions to clients worldwide through platforms like Fiverr and direct engagements.
My work centers on building Retrieval-Augmented Generation systems: designing document ingestion and chunking pipelines, embedding and indexing content in vector databases (Pinecone, Weaviate), and connecting retrieval to large language models both hosted APIs and self-hosted, fine-tuned open-source models (Llama, Qwen, Mistral) run through Ollama for clients who need full control over their data.
Beyond RAG, I build agentic automation with tools like n8n and CrewAI, connecting retrieval and reasoning to real workflows instead of static Q&A. I build systems that are honest about their limits retrieval that cites its sources, and models that say "I don't know" rather than guessing because that's what actually holds up once it's in front of real users, not just a demo.
I care about delivering AI applications that are reliable, scalable, and genuinely useful solving real business problems rather than showcasing technology for its own sake.
🤖 Tech stack :
☑️ RAG
☑️ ChatBot Integration
☑️ LangChain
☑️ LlamaIndex
☑️ Pinecone
☑️ Weaviate
☑️ Chroma
☑️ Ollama
☑️ vLLM
☑️ Llama 3.1
☑️ Qwen3
☑️ Mistral
☑️ n8n
☑️ CrewAI
☑️ Python
☑️ OpenAI API
✨ Why work with me:
* ⚡ I reply within a few hours, every day — you're never left waiting on an update
* 🎯 I scope small and fixed before anything open-ended, so you know exactly what you're paying for before we start
* 🛠️ Every system ships with a short walkthrough so your team actually knows how to use and maintain what I build — not just a handoff
* 🚦 If a pilot doesn't work, we don't move forward to the bigger build — I'd rather tell you it's not the right fit than sell you something that isn't
📩 Send me a sample of your documents or describe the workflow you want automated. I'll reply within 24 hours with a clear yes/no and what a first pilot would look like.
Steps for completing your project
After purchasing the project, send requirements so Sardar can start the project.
Delivery time starts when Sardar receives requirements from you.
Sardar works on your project following the steps below.
Revisions may occur after the delivery date.
Full flatch data set
Please I need the documents or datasets on which you want to implement a model