I will build a custom ai agent or chatbot with openai or groq API in python


About this gig
There's a difference between a chatbot that demos well and an AI system that holds up in production. I build the latter.
I build AI agents in Python that connect to real data, call APIs, use tools, make contextual decisions, and fail gracefully. The hard parts aren't API callstheyre prompt reliability, retrieval quality, latency, and knowing when an AI should not answer.
What I build:
RAG Support Agents grounded in your documentation, FAQs, or database instead of generic GPT responses.
AI Agents with Tool Use agents that query APIs, write to databases, send messages, and trigger workflows.
Voice AI inbound/outbound systems with Twilio, real-time transcription, and AI responses. One system I built recovered 40% of missed inbound calls.
Internal Assistants Slack bots, dashboard assistants, and tools for querying internal data.
AI Pipelines chained LLM workflows for content, documents, classification, and reports.
I work with OpenAI GPT-4o, Groq/Llama 3.3, and Anthropic Claude, choosing based on accuracy, speed, cost, and context requirements.
Tell me what you want the AI to do and what systems it needs to work with. Ill recommend the right architecture and tell you what's realistic.
Get to know Muhammad Z
Python Developer, AI Automation, SaaS Apps and API Integrations
- FromPakistan
- Member sinceSep 2026
- Avg. response time1 hour
Languages
Urdu, English
My Portfolio
FAQ
Do I need to pay for my own OpenAI or Groq API key?
Yes, you'll need your own API key. I'll help you set it up and estimate your monthly costs based on expected usage before we start.
Can it be trained on my company's documents or website?
Yes. Using RAG (retrieval-augmented generation), the AI retrieves relevant chunks of your content before responding, so answers come from your actual data — not GPT's general knowledge.
Where does it run — my website, an app, Slack?
Wherever makes sense for your use case. I deliver a working API endpoint by default, and I can add a web widget, Streamlit UI, or Slack integration depending on the package.
What if the AI gives wrong answers?
That's what prompt engineering and retrieval tuning are for. Part of what I do is test edge cases, catch failure modes, and make sure the system knows when to say "I don't know" instead of guessing.
