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I will build a custom rag chatbot and fine tune llm on your data
Naveed A
About this gig
Tired of ChatGPT hallucinating on your internal docs?
Generic AI tools don't understand your contracts, SOPs, product specs, or customer history
and they leak your data to external APIs.
I build private, accurate AI systems trained on YOUR knowledge base.
What you get:
- RAG pipelines using LangChain, LlamaIndex, Pinecone, or Weaviate
- Fine-tuned open-source LLMs (Llama 3, Mistral, Qwen) via LoRA/QLoRA
- Custom chunking & embedding strategies tuned to your document type
- Production deployment FastAPI, Docker, AWS/Modal/RunPod
- Evaluation reports accuracy, hallucination rate, latency benchmarks
- Stack: Python, PyTorch, Hugging Face, LangChain, OpenAI, Anthropic
Why work with me:
- Production-grade code clean, documented, version-controlled
- Clear communication written updates, no jargon dumps
- Data privacy first local/private deployment options always available
- You own everything full source code, model weights, and architecture
Message me BEFORE ordering with: your document count, file types, and use case. I'll
confirm fit and timeline within 2 hours.
Serious projects only let's build something that actually works in production.
Get to know Naveed A
Naveed A
Custom AI, ML, Data Science systems for your business
- FromPakistan
- Member sinceJan 2023
Languages
English
I build production-grade AI, machine learning, and data science solutions for businesses that need their tech to actually work — not just demo well.
Services include: custom RAG chatbots, LLM fine-tuning (Llama 3, Mistral, Qwen), recommendation systems for e-commerce, predictive ML models, Python notebook debugging, and end-to-end data science pipelines.
Stack: Python, PyTorch, TensorFlow, LangChain, FastAPI, Pandas, Scikit-learn, Hugging Face, Pinecone, Docker. Deployment-ready code, evaluation reports, and full ownership of what we build.
Serious projects only. Message me first to scope.
My Portfolio
FAQ
Will my proprietary data be sent to OpenAI, Anthropic, or any third party?
Only if you choose a hosted-API setup (cheaper, faster). If data privacy is non-negotiable, I deploy with open-source models (Llama 3, Mistral) on your own infrastructure or a private cloud — nothing leaves your environment. I will recommend the right path during our pre-order discussion.
How much will the underlying compute and API costs be on my end?
For a Standard RAG setup: roughly $70–$500/month for vector DB (Pinecone or equivalent), $10–$50/month for embeddings, and $200–$2,000/month for LLM API calls depending on traffic volume. Finetuning is a one-time training cost. I provide a written cost estimate before we start.
Can you fine-tune on my data, or do I need to use RAG?
Both have valid use cases. RAG is better when your knowledge base updates frequently and you need source citations. Fine-tuning is better when you need a specific style, format, or domain vocabulary baked into the model. The Premium package combines both — I'll recommend the right architecture.
How do you measure whether the system actually works?
Every delivery includes an evaluation harness: a held-out test set of question-answer pairs, retrieval accuracy (recall@k), answer faithfulness (groundedness to retrieved context), hallucination rate, and latency benchmarks. You get the numbers, the methodology, and the test set.
What happens after delivery if something breaks in production?
Standard includes 7 days of bug-fix support; Premium includes 30 days plus a 60-minute integration consulting call. After that, I offer ongoing maintenance retainers separately. I do not abandon projects — broken systems hurt my reputation more than yours.
