AI Conversation Bridge

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A reference architecture that connects enterprise messaging apps (LINE WORKS, WeChat, Feishu, etc.) to Workday using AI-powered orchestration. It's built for markets where you need to meet workers in the apps they already use every day.

AI.Conversation.Bridge.Demo.720p.mp4

Why we built this

Enterprise AI usually doesn't fail because of the tech. It fails because it doesn't reach the right people.

In the APJ region (especially China, Japan, and South Korea), getting workers to actually use AI tools comes with a few major roadblocks:

• Regulatory hurdles: You can't just point workers in China to a US-hosted AI or LLMs. US/China policy environments create barriers to this and local regulations sometimes require local models.
• Language and context: Global models often don't understand company-specific jargon or local cultural nuances. Asking for "Golden Week off" needs to actually mean something to the AI.
• Super-app dominance: Workers in China live in WeChat and Feishu. In Japan, it's LINE. In Korea, KakaoTalk. Asking millions of people to download a separate enterprise app just doesn't work.
• Android app availability: The Google Play Store is blocked in China, meaning a huge chunk of the workforce can't even download the standard Workday Android app.

The result? Companies have Workday and want to use AI, but the workers who need it most are left out.

The AI Conversation Bridge flips this around. Instead of forcing workers to log into Workday, it brings Workday directly into their favorite chat apps. It uses local LLMs and infrastructure, so it respects regional rules and digital culture. A worker just sends a message in WeChat, and the AI handles the rest. Workday remains the secure source of truth, but the front door is wherever the worker already is.

While we built this with APJ in mind, the pattern works anywhere you want to use your own LLMs or chat platforms.

Architecture

Chat App  ←→  Chat Connector  ←→  Flowise (the bridge)  ←→  MCP Server  ←→  Workday

The project has three main pieces. Flowise is the brain — it connects to the LLMs, figures out what the user wants, and calls Workday tools via MCP. The other two components act as its ears and hands: the Chat Connector listens to the chat apps, and the MCP Server executes actions in Workday.

(For more details on boundaries and intended usage, check out docs/architecture.md.)

Component	What it does	Where it lives
Flowise Flows	Handles LLM orchestration, intent recognition, and MCP tool calling.	flowise/
Chat Connector	A two-way adapter that receives messages from chat platforms and sends the AI's responses back.	chat-connector/
Demo MCP Server	Mock Workday tools for testing and development. (Swap this out for the Workday Agent Gateway in production).	mcp-demo-server/

Quick Start

What you'll need

• A container hosting platform with public HTTPS endpoints (like Google Cloud Run)
• A Flowise instance (cloud or self-hosted, as long as it's public-facing)
• LINE WORKS Bot credentials (for the chat connector)

Note: Everything needs to be deployed to a public-facing cloud environment. We use Google Cloud Run in these examples, but any container platform works (AWS App Runner, Azure Container Apps, Alibaba Cloud Elastic Container Instance, Tencent Kubernetes Engine, etc.).

1. Clone the repo

git clone https://github.com/your-org/ai-conversation-bridge.git
cd ai-conversation-bridge

2. Deploy the demo MCP server

gcloud run deploy mcp-demo-server \
  --source mcp-demo-server

Going to production? Replace this demo server with Workday's official MCP endpoints via Agent Gateway for real enterprise-grade security and authentication. Don't forget to update the MCP configuration in your Flowise flow!

3. Import the Flowise flow

1. Open your Flowise instance.
2. Go to Agent Flows → Add New → Settings (⚙️) → Load Agentflow.
3. Import flowise/flows/workday-mcp-agent.json.
4. Set up your LLM credentials.
5. Update the MCP server URL in the Agent node's Custom MCP tool to point to your deployed demo server (e.g., https://mcp-demo-server-abc123.us-west1.run.app/mcp).

(Need more help? See flowise/README.md.)

4. Deploy the chat connector

gcloud run deploy chat-connector \
  --source chat-connector

Important: Don't forget to set your environment variables in the Cloud Run console after deploying! You will need to configure your AI provider (like CHAT_PROVIDER and FLOWISE_API_URL) as well as your LINE WORKS bot credentials. See chat-connector/.env.example for the full list of required variables.

5. Connect LINE WORKS

Set your LINE WORKS Bot webhook URL to your new chat connector's public URL + /callback (e.g., https://chat-connector-abc123.us-west1.run.app/callback).

Chat Providers

The chat connector supports two AI backends out of the box:

Provider	When to use it	Config
Flowise (default)	Production — gives you full orchestration and MCP tool calling.	CHAT_PROVIDER=flowise
OpenRouter	Demos/experimenting — great for quick testing with any LLM without setting up Flowise.	CHAT_PROVIDER=openrouter

Demo MCP Tools

The demo MCP server comes with mock Workday tools and data so you can test the whole pipeline. When you're ready for production, just swap it out for Workday's official MCP endpoints.

Tool	What it does
find_employee_id_by_name	Look up an employee's worker ID by name
get_current_user_info	Get the current user's profile
get_current_user_time_off_balance	Get the current user's leave balances
get_current_user_time_off_history	Get the current user's leave request history
get_time_off_balance	Get leave balances for any worker by ID
get_direct_reports	List direct reports for a manager
get_more_employee_data	Get extended employee data
get_my_time_off_eligibility	Check which leave types the current user can request
get_personal_information	Get personal info (address, emergency contact)
get_today_date_and_day_of_week	Get the current date and time
request_my_time_off	Submit a time-off request for the current user

Fun fact: The mock data includes workers across China, Japan, and South Korea with localized names and currencies!

Project Structure

ai-conversation-bridge/
├── chat-connector/          # Webhook adapter (Flask, Python)
│   ├── app/services/        # Messaging adapters (LINE WORKS) + AI clients
│   ├── Dockerfile
│   └── .env.example
├── flowise/                 # Flow templates (the core bridge logic)
│   ├── flows/               # Exportable Flowise flow JSON files
│   └── screenshots/
├── mcp-demo-server/         # Demo Workday MCP server
│   ├── mock_data/           # Sample worker, time-off, and pay data
│   ├── Dockerfile
│   └── .env.example
├── docs/                    # Architecture and setup documentation
├── scripts/                 # Local dev setup (setup.sh) and cloud deploy (deploy-cloud-run.sh)
├── docker-compose.yml       # Container build/test utility
└── .github/                 # Issue templates, PR template

Documentation

• Architecture — Detailed system design and request flow
• Setup Guide — Step-by-step setup for each component
• Enterprise Hardening Guide — Security, reliability, and operational recommendations for production
• Flowise Configuration — How to import and configure the flow templates
• Contributing — How to contribute to this project

License

This project is licensed under the Apache License 2.0 — see LICENSE for details.