1. Builder Docs
  2. Builder Guides
  3. Examples
  4. Employee Assistant

Example: Building an Employee Assistant

This walkthrough builds a complete Employee Assistant AI Employee using Ema's Employee Experience Suite. The finished assistant handles greetings, answers personalized HR policy questions, and falls back to document search for general queries.

What You Will Build

A chatbot AI Employee that:

  • Classifies incoming messages by intent (greetings versus policy questions)
  • Summarizes conversation context for multi-turn interactions
  • Extracts personalization tags (country, band) from the user profile
  • Rewrites queries for optimal document retrieval
  • Searches uploaded HR documents and returns cited answers

Prerequisites

  • Access to the GWE platform
  • One or more HR policy documents (PDF, DOCX, or TXT) ready to upload

Locate the Chatbot Template Tile

  1. Navigate to the GWE platform.
  2. Locate the tile labeled GWE Chatbot Persona.
  3. Click Create and save and assign a name to your assistant (for example, "Employee Assistant").

Access the Workflow Builder

  1. Click Go to Workflow builder on the confirmation screen.
  2. A Chat Trigger block is automatically present. This block handles all incoming user conversations.

Step 1: Add a Categorize Conversations and Route Agent

The Categorize Conversations and Route agent classifies incoming queries so the workflow can route them to the correct path.

  1. Add the Categorize Conversations and Route agent to the canvas.
  2. Connect the chat trigger's chat conversation output to the conversation input of the categorizer.
  3. Open the categorizer's configuration panel on the right.
  4. Click Add a Category and define the following intents:
    • Greetings/Feature Inquiries -- example phrases: "Hi", "What can you do?", "Hello there!"
    • Fallback (default category) -- catches any query that does not match a defined category. In this example, fallback handles general HR questions that require document lookup.

Step 2: Maintain Context with a Conversation Summarizer

The Conversation Summarizer condenses the chat history so downstream agents receive concise context instead of the full transcript.

  1. Add the Conversation Summarizer agent.
  2. Connect the chat trigger's chat conversation output to its conversation input.
  3. Set a Trigger When condition so this agent runs only when the Fallback category is active:
    • Click the agent, select Add Trigger When, and choose the Fallback category from the categorizer.

Step 3: Extract Personalization Tags

The Tag Extractor pulls metadata such as country and band from the user's profile, enabling personalized responses.

  1. Add the Tag Extractor agent.
  2. Connect the chat trigger's chat conversation output to its conversation input.

Step 4: Respond to Greetings

  1. Add a Respond to a Query agent to handle queries in the Greetings category.
  2. Connect the chat trigger's user query to this agent's query input.
  3. In the configuration panel, write instructions that provide a friendly introduction and list the assistant's capabilities.
  4. Rename the agent to something descriptive (for example, "Greetings Responder").
  5. Set a Trigger When condition so this agent fires only for the Greetings/Feature Inquiries category.

Step 5: Rewrite the Query for the Fallback Path

A Query Rewriter reformulates the user's question using conversation context and personalization tags, producing a search-optimized query.

  1. Add a Respond to a Query agent and rename it Query Rewriter.
  2. Write instructions that tell the agent to act as a query rewriter -- combining the summarized conversation and user tags into a single, coherent search query.
  3. Connect the summarized conversation output from the Conversation Summarizer to the Query Rewriter's query input.
  4. Connect the user tags output from the Tag Extractor to its user tags input.

Step 6: Perform Knowledge Search Using the Rewritten Query

  1. Add the Knowledge Search agent.
  2. Connect the query output from the Query Rewriter to the Knowledge Search agent's query input.
  3. Upload relevant HR documents:
    • In the configuration panel, click Add Data Source.
    • Select Upload Documents and upload your files (for example, leave policies, travel guidelines, performance processes).
  4. After upload, click Manage next to the document folder to add or remove documents and assign metadata tags for improved retrieval accuracy.

Step 7: Respond Using Search Results

  1. Add a Respond to a Query agent.

  2. Connect:

    • Query from the Query Rewriter
    • User Tags from the Tag Extractor
    • Search Results from the Knowledge Search agent

  3. Write instructions that define the assistant's tone and formatting. For example:

    "You are a helpful and professional HR assistant. Use a conversational tone. Format the response with clear bullet points when listing policies or steps."

Step 8: Publish Workflow Outputs

Publish the final outputs so they appear in the chatbot's response interface:

  1. Select the Greetings Responder agent and enable Publish this as a workflow output.
  2. Select the Respond to a Query agent (from Step 7) and enable Publish this as a workflow output.

The correct output is rendered to the end user based on which intent path the query follows.

Step 9: Save and Activate

  1. Review all connections and agent configurations.
  2. Click Save Changes in the workflow builder.
  3. Activate the AI Employee for testing.

Step 10: Customize the Assistant Identity

On the Configuration Page, personalize your AI Employee:

  • Assistant Name -- click the name field to rename (for example, "HR Buddy" or "AskEma").
  • Icon -- click the avatar to upload a custom icon.
  • Welcome Message -- edit the message first-time users see (for example, "Hi! I'm here to help with your HR questions.").
  • Conversation Starter Buttons -- add buttons that appear alongside the welcome message to simplify common queries.

Step 11: Test Your Employee Assistant

  1. Click the chat icon at the bottom-right of the screen to open the preview window.
  2. Try different queries to verify:
    • The categorizer routes greetings and policy questions correctly.
    • Search results are accurate and well-formatted.
    • Personalization tags influence the response when expected.
  3. Iterate on agent instructions as needed before going live.

Next Steps

Last updated: Jul 3, 2026