Codenetix AI
Projects

Real AI Systems You Will Build

Participants graduate with a portfolio of production‑style AI applications that demonstrate real implementation experience, not just theory or toy notebooks.

Enterprise Knowledge Assistant

RAG‑powered chatbot that answers questions using company documents, policies, and knowledge bases.

  • RAG architecture and vector search
  • Document ingestion and chunking
  • Azure OpenAI + vector DB integration
  • Azure OpenAI
  • LangChain
  • Chroma / Pinecone

AI Customer Support Assistant

Multi‑channel support copilot that helps agents respond faster and escalates complex queries.

  • Conversation history management
  • Knowledge base lookups
  • Agent assist workflows
  • FastAPI
  • React
  • Azure AI

AI Legal / Policy Document Analyzer

System that extracts key clauses, risks, and obligations from long contracts or policy documents.

  • Chunking and semantic search
  • Targeted question answering
  • Red‑flag and risk highlighting
  • LLMs
  • Embeddings
  • Document AI

AI Research Assistant

Assistant that reads multiple sources, synthesizes information, and produces structured research briefs.

  • Multi‑document summarization
  • Citation and reference tracking
  • Prompt‑chained reasoning
  • Azure OpenAI
  • LangChain
  • Web APIs

AI Data Analytics Copilot

Natural‑language interface over analytics data that can generate insights and visualizations.

  • SQL + natural‑language queries
  • Chart and dashboard generation
  • Business‑oriented insights
  • LLMs
  • SQL
  • BI / visualization

AI Coding / DevOps Assistant

Internal engineering copilot that helps with code explanation, documentation, and deployment automation.

  • Repository indexing and search
  • DevOps workflow suggestions
  • Secure code practices
  • Azure Repos / GitHub
  • Azure Pipelines
  • LLM agents

Portfolio at Graduation

By the end of the fellowship, participants will have 10–15 portfolio‑ready AI systems that reflect real business problems and modern implementation patterns.

  • End‑to‑end project repos (code + infra)
  • Deployed demos where possible
  • Architecture diagrams and decision records
  • Case‑study style write‑ups for interviews

Why This Matters to Employers

Employers can see clear evidence that you can design, build, and deploy AI workloads in realistic settings, instead of only completing isolated tutorials.

  • Demonstrates hands‑on Azure AI experience
  • Shows understanding of RAG & LLM safety
  • Highlights collaboration and product thinking