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Fiverr Pro
English
$
USD
I will build your rag ai agent for business data
Nathan Asif
About this gig
I build custom RAG AI agents that connect language models to your business data so they can provide grounded answers and, when needed, perform useful actions through APIs and tools.
I can work with documents, knowledge bases, websites, databases and external services.
What I can build:
- RAG chatbots and knowledge assistants
- LangGraph AI agents and stateful workflows
- Business data and document retrieval
- Vector database integration
- API and tool-calling agents
- Conversation memory and citations
- OpenAI, Gemini and other LLM integrations
- FastAPI backends and web interfaces
- Existing application integrations
- Deployment and documentation
My portfolio includes ORCUBEAI, a multi-LLM RAG SaaS platform, along with systems involving LangGraph, multimodal AI and agent verification.
I focus on reliable retrieval, clean architecture and maintainable code rather than simple prompt wrappers.
Please message me before ordering for large datasets, complex workflows, multiple integrations or existing codebases.
Third-party API, model, hosting and infrastructure costs are not included unless agreed.
Get to know Nathan Asif
Nathan Asif
AI SaaS and Automation Engineer RAG and AI Agents
- FromPakistan
- Member sinceFeb 2025
- Avg. response time1 hour
Languages
English, Urdu, French, German, Spanish
I build production-ready AI systems, SaaS products, and automation solutions for businesses that need reliable software beyond simple demos.
My work includes AI agents, RAG systems, conversational and multimodal AI, workflow automation, API integrations, and full-stack applications.
I work with Python, FastAPI, Next.js, React, OpenAI, Gemini, LangChain, LangGraph, Supabase, PostgreSQL, and Node.js.
I focus on clean architecture, dependable integrations, and maintainable systems that solve real business problems.
My Portfolio
FAQ
What data can the RAG agent use?
I can work with PDFs, documents, websites, knowledge bases, structured data, databases and APIs. The best ingestion approach depends on your data format and volume.
What is the difference between a RAG chatbot and an AI agent?
A RAG chatbot retrieves relevant knowledge before answering. An AI agent can additionally maintain state, call tools or APIs and perform multi-step actions.
Do you use LangGraph for every project?
No. I use LangGraph when stateful workflows, branching, tool usage or multi-step agent behavior justify it. Simple RAG systems do not need unnecessary complexity.
Which AI models can you integrate?
I can work with OpenAI, Gemini and other suitable hosted or open-source models depending on your requirements, infrastructure, privacy needs and usage.
Can the AI agent call my APIs or business tools?
Yes. Standard includes one API/tool integration, while Premium supports more advanced tool-calling workflows and up to three integrations.
Can you integrate the AI agent into my existing application?
Yes. Existing-application integration is included in Premium after reviewing the application's architecture and integration requirements.
Will I receive the source code?
Yes. Source code for the work included in the agreed package is provided.
Do you provide deployment?
Deployment is included where stated in the package and when the target infrastructure is agreed in advance. Hosting and third-party service costs are separate.
Can you work with private business data?
Yes. The architecture can be selected according to your privacy and security requirements. Contact me first if the project involves regulated or highly sensitive data.
What if my project does not fit one of the packages?
Send me the requirements first. Large RAG systems, multi-agent workflows, complex integrations and custom SaaS implementations can be handled through a custom offer.
