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$
USD
I will build custom ai agents using crewai and fastapi
Vaishnavi
About this gig
Looking to integrate custom AI agents, zero-hallucination RAG pipelines, or production-ready FastAPI backends into your app?
I specialize in engineering autonomous LLM workflows and secure API data gateways using Python, CrewAI, LangChain, and ChromaDB.
What I Can Build For You:
- Multi-Agent Workflows: Multi-role AI pipelines (CrewAI/LangGraph) automating complex tasks and data loops.
- Zero-Hallucination RAG Systems: Secure enterprise knowledge bases using ChromaDB with strict context-trapping guardrails.
- High-Speed FastAPI Gateways: Asynchronous, type-safe API endpoints built with Pydantic V2 to validate inputs and connect LLMs safely.
- Multi-LLM Orchestration: Integration with OpenAI (GPT-4o), Claude, and local vector indexing.
Why Work With Me?
- Clean, production-ready Python code with clear comments.
- Focus on data security, zero-hallucination boundaries, and token efficiency.
- Self-documenting OpenAPI (Swagger UI) endpoints for easy testing.
Let's turn your AI idea into a scalable pipeline. Please message me with your project details before placing an order!
Get to know Vaishnavi
Vaishnavi
AI Engineer specializing in CrewAI, RAG, and FastAPI
- FromIndia
- Member sinceJan 2025
- Avg. response time2 hours
Languages
English
Hi, I'm an AI & Data Science Engineer specializing in autonomous AI workflows, zero-hallucination RAG pipelines, and high-performance API backends.
My Core Stack:
• Agents & RAG: CrewAI, LangChain, LangGraph, ChromaDB
• APIs & Backend: FastAPI, Pydantic V2
• Machine Learning: Python, PyTorch, NLTK
Whether you need multi-agent orchestration, custom document intelligence, or production-ready API integration for your product, I deliver clean, documented code built for scale.
Message me to discuss your project requirements!
My Portfolio
FAQ
What do you need from me to get started on my AI project?
I will need a brief overview of your business logic, target workflows, and any API keys required (such as OpenAI, Anthropic, or external services). If we are building a RAG system, sharing sample data files (PDFs, CSVs, or text documents) will help us set up your vector database quickly.
How do you ensure the RAG system does not hallucinate?
I implement strict semantic context-trapping guardrails using ChromaDB and vector similarity thresholds. If the retrieved documents do not contain the answer to a query, the model is hard-coded to report 'Information not found' or escalate to a human rather than fabricating a response.
Can you integrate the AI pipeline into my existing FastAPI/Python backend?
Yes! I build asynchronous, type-safe FastAPI endpoints with Pydantic schema validation. I can integrate new AI endpoints cleanly into your existing codebase or build a standalone microservice gateway from scratch.
Do you provide source code and documentation?
Always. All packages include fully commented Python source code and self-documenting OpenAPI (Swagger UI) endpoints so your team can test, run, and maintain the pipeline effortlessly.
