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$
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I will build a custom ai chatbot trained on your data using gpt and rag
Asif Karim
About this gig
Stop losing customers to generic, "hallucinating" AI. I build custom AI chatbots from your own data using GPT and RAG to give users instant, accurate answers based on your unique company knowledge.
Most bots fail because they rely on general data. I specialize in RAG (Retrieval-Augmented Generation), ensuring your assistant provides brand-aligned responses derived directly from your PDFs, websites, or databases.
️ What I Offer:
- Document-Trained Bots: Feed it PDFs, Docx, or CSVs.
- Website AI Chatbots: A smart widget that knows your entire site.
- AI Knowledge Bases: Internal tools for employees to query SOPs.
- SaaS & Database Integration: Natural language querying for SQL/NoSQL.
Key Features:
- Advanced Stack: LangChain, Python, & FastAPI.
- Vector Databases: Pinecone, FAISS, or ChromaDB for fast retrieval.
- Multi-Source Ingest: PDFs, Notion, Google Drive, & URLs.
- Seamless Deployment: Slack, WhatsApp, or Website widgets.
- Secure & Private: Enterprise-grade data handling.
- I dont just "prompt" AI; I architect production-ready systems using GPT-4 and Gemini. Message me before ordering to discuss your data and goals!
Get to know Asif Karim
Asif Karim
AI ML Engineer Python Prompt Engineering
- FromPakistan
- Member sinceMar 2025
Languages
Urdu, English
AI ML Engineer specializing in Python and Prompt Engineering, helping businesses turn AI ideas into production ready systems. I design and deploy scalable machine learning models, LLM applications, and intelligent automation solutions that deliver measurable results. Experienced in GPT based apps, NLP, and end to end AI deployment. I focus on building reliable, secure, and business driven AI solutions that create real impact.
My Portfolio
FAQ
Q1: What is a RAG system?
A RAG (Retrieval-Augmented Generation) system retrieves relevant documents and combines them with generative AI (LLMs) to provide accurate, contextual answers.
Q2: What documents can this system handle?
It can handle PDFs, text files, and DOCX files. Additional formats can be added on request.
Q3: Which vector databases do you use?
I use FAISS, ChromaDB, Pinecone, or Weaviate based on your preference and use case.
Q4: Can this system work offline or with open-source models?
Yes, I can integrate Hugging Face models that run locally if you don’t want to use cloud APIs like OpenAI.
Q5: Can you deploy the app for me?
Yes! Deployment via Docker or a step-by-step guide can be included in the Premium package or as an extra.
Q6: Will the code be documented and reusable?
Absolutely. The code will be clean, modular, and include comments and setup instructions.
