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Fiverr Pro
English
$
USD
I will build a rag chatbot trained on your docs, website or custom knowledge base
Jansher K
About this gig
Your business has valuable knowledge sitting in PDFs, docs, FAQs, and databases that nobody can access fast enough. I build RAG chatbots that give instant, accurate answers from YOUR data.
WHAT I BUILD:
- Custom chatbot trained on your PDFs, docs, website or database
- - AI customer support bot that knows your products and policies
- - Internal knowledge base assistant for your team
- - Website chatbot that answers questions from your content
- - CRM and sales bot with your product catalog and pricing
TECH STACK:
OpenAI, Claude, Gemini, LangChain, LlamaIndex, Pinecone, Qdrant, Supabase, PostgreSQL, n8n
DEPLOY ANYWHERE:
Website widget, WhatsApp, Telegram, Slack, API endpoint, or CRM integration
WHY CHOOSE ME:
- Accurate answers, not hallucinations - your data is the source of truth
- - Private and secure - your data stays yours
- - Message me first to discuss your use case.
Get to know Jansher K
Jansher K
Automate Everything
- FromPakistan
- Member sinceJul 2016
- Avg. response time1 hour
- Last delivery2 years
Languages
English
I’m Jansher Khan, a full-stack software engineer and AI automation specialist focused on building intelligent, scalable systems for modern businesses. I design agentic AI workflows, automation pipelines, and API integrations that reduce manual work, improve efficiency, and scale operations. My solutions are security-first, open-source focused, and production-ready, deployed on reliable VPS infrastructure. I work with startups and teams to turn complex processes into clean, maintainable, and cost-effective automation systems that deliver real business results.
FAQ
Q1: What information do you need to start building my AI RAG agent?
I need your use case, data source (documents, database, APIs, or URLs), preferred AI model if any, and deployment preference. If required, access credentials can be shared securely after the order starts.
Q2: How is a RAG agent different from a normal AI chatbot?
A RAG agent retrieves answers from your own data using vector databases and then generates responses using an AI model. Unlike basic chatbots, it provides accurate, context-aware results and can use tools, APIs, and automation workflows.
Q3: Do you support n8n AI automation with RAG agents?
Yes. I integrate RAG agents with n8n to create automated workflows, triggers, conditional logic, API calls, and data syncing with your existing systems.
Q4: Which databases and vector stores can you work with?
I support PostgreSQL, Supabase, Qdrant, Pinecone, and other vector databases. The final choice depends on your data size, performance needs, and deployment environment.
Q5: Can the AI agent be deployed in my own environment?
Yes. The system can be deployed on your cloud, server, or SaaS environment to ensure data privacy, security, and full ownership.
Q6: Is my data secure and confidential?
Yes. Your data is used only for your project. I do not reuse, store, or share client data outside the agreed deployment environment.
Q7 (Technical): How do you design agentic RAG architectures?
I design agentic systems using retrieval pipelines, embedding strategies, vector search, memory management, tool usage, and reasoning chains. Depending on the project, I use LangChain, LlamaIndex, n8n workflows, and custom logic to ensure scalable and production-ready performance.
Q8 (Technical): Can you handle large datasets and real-time updates?
Yes. I support chunking strategies, incremental data syncing, scheduled ingestion, and optimized vector indexing to handle large datasets and near real-time updates efficiently.
Q9: Do you offer post-delivery support or future enhancements?
Yes. Each package includes revisions, and long-term support, scaling, or feature enhancements can be provided as a separate service.
