AI Conversation Bridge

<p align="center">_{English | <a href="i18n/zh-Hans/README.md">简体中文</a> | <a href="i18n/zh-Hant/README.md">繁體中文</a> | <a href="i18n/ja/README.md">日本語</a> | <a href="i18n/ko/README.md">한국어</a>}</p>

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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.

https://github.com/user-attachments/assets/9b1ea495-5f23-4ae6-b735-18874acdd327

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 and/or DingTalk robot access (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 AI_PROVIDER and FLOWISE_API_URL) as well as any chat channel settings. See chat-connector/.env.example for the full list of variables.

5. Connect Chat Channels

Set your chat platform callback URLs to the channel-specific endpoints:

• LINE WORKS: https://chat-connector-abc123.us-west1.run.app/lineworks/callback
• DingTalk HTTP robot: https://chat-connector-abc123.us-west1.run.app/dingtalk/callback

The legacy /callback path is still accepted as a LINE WORKS alias for existing deployments.

AI Providers

The chat connector supports two AI backends out of the box. CHAT_PROVIDER is still accepted as a fallback, but new deployments should use AI_PROVIDER.

| Provider | When to use it | Config | | --------------------- | ------------------------------------------------------------------------------------ | -------------------------- | | Flowise (default) | Production — gives you full orchestration and MCP tool calling. | AI_PROVIDER=flowise | | OpenRouter | Demos/experimenting — great for quick testing with any LLM without setting up Flowise. | AI_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, DingTalk) + 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
• Changelog — Tagged releases (v0.x; reference architecture, no stability promise)

License

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