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I will build a custom rag ai chatbot trained on your documents and data
Malaika Malik
À propos de ce service
Need an AI chatbot that can answer questions using your own documents and business knowledge?
I build custom RAG AI chatbots and knowledge-base assistants that connect LLMs with your private data to deliver accurate, context-aware, source-grounded answers.
I can build:
- RAG chatbots for PDFs, DOCX, TXT, FAQs and business documents
- Private knowledge-base and employee assistants
- Customer support AI chatbots
- Document ingestion, chunking and embedding pipelines
- Semantic search and vector database integration
- Source citations and grounded responses
- Multi-document and multi-source retrieval
- Conversation memory and contextual chat
- Authentication, APIs and database integrations
- Guardrails, evaluation and hallucination reduction
- FastAPI, React or Next.js interfaces
- Dockerized cloud deployment
Technologies: OpenAI, Claude, Gemini, Hugging Face, LangChain, LangGraph, ChromaDB, Qdrant, Pinecone, Sentence Transformers, Python, FastAPI, React, Next.js, Docker, AWS and Azure.
You will receive clean source code, tested retrieval, documentation and deployment support.
Please message me before ordering to discuss your data, integrations and requirements.
Découvrez Malaika Malik
Malaika Malik
Full Stack Generative AI and AI Automation Engineer
- DePakistan
- Membre depuisjuil. 2026
- Temps de réponse moy.1 heure
Langues
Anglais, Ourdou
I’m a Full-Stack Generative AI and AI Automation Engineer specializing in production-ready AI applications, intelligent agents, RAG systems, chatbots, and business workflow automation. I work with OpenAI, Claude, LangChain, LangGraph, ChromaDB, FastAPI, Node.js, React, Next.js, n8n, Zapier, Make.com, APIs, webhooks, Docker, AWS, and Azure. I deliver secure, scalable solutions with clean architecture, reliable integrations, optimized workflows, and cloud deployment tailored to real business needs.
FAQ
What is a RAG AI chatbot?
RAG stands for Retrieval-Augmented Generation. The chatbot searches your knowledge base for relevant information before generating an answer, helping responses stay grounded in your business data.
What type of data can I use?
I can work with PDFs, Word documents, text files, FAQs, website content, databases and other structured or unstructured data depending on your project requirements.
Can the chatbot answer questions only from my documents?
Yes. I can configure the system to prioritize your private knowledge base and add guardrails so it avoids answering unsupported questions when relevant information is unavailable.
Which AI models can you integrate?
I can work with OpenAI, Claude, Gemini, Hugging Face models and other supported LLM providers depending on your budget, privacy requirements and use case.
Which vector databases can you use?
I primarily work with ChromaDB and can also integrate solutions such as Qdrant or Pinecone depending on scalability, hosting and project requirements.
Can you integrate the chatbot into my website?
Yes. I can provide an API or integrate the RAG chatbot with a React, Next.js or existing web application depending on your package and requirements.
Do you provide source code?
Yes. Source code is included according to the selected package together with setup instructions and relevant documentation.
Can you deploy the RAG chatbot?
Yes. I can Dockerize and deploy the application to supported cloud platforms such as AWS, Azure, Render or Railway. Hosting and third-party service charges are paid separately by the client.
Are OpenAI or hosting costs included?
No. LLM API usage, vector database plans, cloud hosting and other third-party service charges are paid directly by the client.
Do you guarantee zero hallucinations?
No AI system can guarantee zero incorrect responses. I can improve reliability through strong retrieval, source grounding, similarity thresholds, guardrails, evaluation and fallback behavior.
