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I will build a rag ai chatbot mobile app for your documents and knowledge base
Ali
About this gig
Need an AI chatbot that answers from your own documents, or business knowledge instead of giving generic responses?
I build complete RAG-powered mobile applications for Android and iOS using Flutter, allowing users to ask questions from PDFs, documents, company knowledge, policies, manuals, or custom business data and receive accurate, source-grounded AI responses.
You can use the chatbot for:
- Customer support
- Internal company knowledge
- PDF/document Q&A
- Product or policy questions
- Employee training and SOPs
- Website knowledge assistants
What I can include:
- PDF, DOCX, TXT, CSV and apps data ingestion
- OpenAI, Claude, Gemini, Groq or compatible LLMs
- LangChain or LlamaIndex RAG pipeline
- Qdrant, Pinecone, ChromaDB or pgvector
- Semantic or hybrid search
- Source citations and conversation memory
- FastAPI backend and API integration
- Responsive chat interface
- Authentication, deployment and Docker setup
- Full source code and documentation
I focus on practical, business-ready RAG systems with clean architecture and reliable retrieval.
Please message me before ordering so I can review your data source, required integrations and best package for your project.
Get to know Ali
Ali
Flutter Developer, AI Mobile Apps for Android and iOS
- FromPakistan
- Member sinceJun 2025
- Avg. response time1 hour
- Last delivery1 week
Languages
Urdu, Pashto, English
Hi! I'm a Flutter developer with 3+ years of experience building AI powered mobile apps for Android and iOS.
I help startups and small businesses turn ideas into clean, fast and easy-to-use apps.
I can build your app from scratch, add ChatGPT or Gemini chatbots, convert Figma to Flutter, and fix app bugs. I work with Firebase, Supabase, Node.js and mongoDB.
I keep communication clear, share regular updates and deliver on time with full source code.
Message me with your app idea and let's build it together!
My Portfolio
FAQ
What is a RAG chatbot?
A RAG chatbot connects an AI model to your own documents, website content, FAQs, policies, or other knowledge sources. Instead of relying only on general AI knowledge, it retrieves relevant information from your data before generating an answer.
What type of data can you connect to the chatbot?
I can work with sources such as PDF, DOCX, TXT, CSV, website content, FAQs, manuals, policies, SOPs, product documentation and other structured or unstructured business information.
Which AI models can you use?
Depending on your requirements, I can integrate OpenAI, Claude, Gemini, Groq or other compatible LLM providers. I can recommend an option based on accuracy, speed, privacy and API cost.
Which vector databases do you support?
I can work with Qdrant, Pinecone, ChromaDB, pgvector and similar vector databases. The best choice depends on your data size, hosting requirements and expected usage.
Can the chatbot show where an answer came from?
Yes. Source citations can be included so users can see which document or source was used to generate an answer. This is particularly useful for internal knowledge bases, policies, research and business documentation.
Can you integrate the RAG chatbot into my existing application?
Yes. I can provide an API or integrate the chatbot with an existing application depending on your current technology and project scope.
Will I receive the source code?
Yes, source code can be included in the project package along with setup instructions and documentation so you are not locked into a closed platform.
Can you deploy the chatbot for me?
Yes. Deployment can be included depending on the selected package and hosting environment. Please message me with your preferred platform or current infrastructure before ordering.
Will the AI always give perfect answers?
No AI system can responsibly guarantee perfect answers in every situation. I focus on strong retrieval, appropriate prompting, source grounding and testing to reduce irrelevant or unsupported responses.
