USER
                          |
                    Voice / Text
                          |
                          v
                 Speech-to-Text
                          |
                          v
                    FastAPI API
                          |
                          v
                 Agent / LLM Router
                          |
          +---------------+----------------+
          |               |                |
          v               v                v
        RAG             MCP             General
       Agent            Tools             LLM
          |               |
          v               v
   Facility Docs      Live Facility
   Knowledge Base         Data
          |               |
          +-------+-------+
                  |
                  v
            Reasoning Layer
                  |
                  v
          Decision / Response
                  |
          +-------+--------+
          |                |
          v                v
     MCP Action           Answer
          |                |
          v                v
 Maintenance Request    Text-to-Speech
                              |
                              v
                         Voice Response

Technology Stack
Backend
Python
FastAPI
Uvicorn
Agent Orchestration
LangGraph
LLM-based routing
Agentic workflow
LLM
Google Gemini
RAG
LangChain
ChromaDB
HuggingFace Embeddings
Semantic retrieval
MCP
Model Context Protocol
MCP Server
MCP Client
Facility operation tools
Voice
SpeechRecognition
Browser Speech Recognition / Speech-to-Text
Browser Text-to-Speech
Frontend
HTML
CSS
JavaScript
Testing
Pytest

Conclusion

The Nectar AI Facility Agent demonstrates an autonomous AI workflow for intelligent facility operations.

The system combines:

Speech-to-Text
Text-to-Speech
LLM reasoning
Intelligent routing
RAG
Vector search
MCP
Tool calling
Synthetic facility data
Multi-step reasoning
Controlled operational actions
Confirmation-based safety
Automated testing

Conclusion

The Nectar AI Facility Agent demonstrates an autonomous AI workflow for intelligent facility operations.

The system combines:

Speech-to-Text
Text-to-Speech
LLM reasoning
Intelligent routing
RAG
Vector search
MCP
Tool calling
Synthetic facility data
Multi-step reasoning
Controlled operational actions
Confirmation-based safety
Automated testing

Conclusion

The Nectar AI Facility Agent demonstrates an autonomous AI workflow for intelligent facility operations.

The system combines:

Speech-to-Text
Text-to-Speech
LLM reasoning
Intelligent routing
RAG
Vector search
MCP
Tool calling
Synthetic facility data
Multi-step reasoning
Controlled operational actions
Confirmation-based safety
Automated testing

The key objective is to demonstrate how an AI agent can move beyond simple question answering and autonomously investigate facility problems using both organizational knowledge and live operational data while maintaining safety around operational actions.