| # Multi-Domain Support Conversations Dataset |
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| This dataset combines **600 realistic customer support conversations** from six different domains: |
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| - **Customer Service** (100 conversations) |
| - **E-commerce** (100 conversations) |
| - **Financial Support** (100 conversations) |
| - **HR / Onboarding** (100 conversations) |
| - **Medical Helpdesk** (100 conversations) |
| - **Technical Support** (100 conversations) |
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| Each dialogue is exactly 11 turns long and covers a wide variety of issues, making it ideal for training general-purpose AI assistants, chatbots, and customer support models. |
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| ## Dataset Structure |
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| Each conversation is stored as a JSON object with the following fields: |
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| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | integer | Unique identifier (1–600, grouped by domain) | |
| | `domain` | string | Original domain (e.g., `"customer service"`, `"e-commerce"`) | |
| | `problem` | string | Short description of the customer's issue | |
| | `customer_type` | string | Customer persona (see full list below) | |
| | `dialogue` | array | List of alternating customer/agent messages | |
| | `dialogue[].role` | string | `"customer"` or `"agent"` | |
| | `dialogue[].text` | string | The message content | |
| | `resolution` | string | One-sentence summary of how the issue was resolved | |
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| ## Customer Personas (with descriptions) |
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| The dataset includes six distinct customer types, evenly distributed across each domain in a fixed cycle (conversations 1–6 contain one of each type, and the pattern repeats every 6 conversations up to 100): |
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| - **frustrated user** – Emotionally charged, expects fast resolution, may be abrupt. |
| - **confused beginner** – Low technical literacy, needs step-by-step guidance, asks basic questions. |
| - **impatient executive** – Busy professional, direct, demands efficiency and authoritative responses. |
| - **elderly user** – Older, patient, requires clear explanations and warm treatment. |
| - **tech-savvy user** – High technical knowledge, uses precise terminology, wants exact details. |
| - **calm and patient user** – Collaborative, easy-going, follows instructions without friction. |
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| This balanced distribution ensures your model learns to handle a wide range of communication styles and needs. |
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| ## Example Conversation |
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| Below is a sample conversation from the **customer service** domain (customer type: impatient executive): |
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| ```json |
| { |
| "id": 1, |
| "domain": "customer service", |
| "problem": "order never arrived", |
| "customer_type": "impatient executive", |
| "dialogue": [ |
| { |
| "role": "customer", |
| "text": "I placed an order 10 days ago and it still hasn't arrived. This is completely unacceptable." |
| }, |
| { |
| "role": "agent", |
| "text": "I sincerely apologize for this delay. I'll look into this immediately. Could you provide your order number?" |
| }, |
| { |
| "role": "customer", |
| "text": "Order number is #ORD-88421. I need this resolved today." |
| }, |
| { |
| "role": "agent", |
| "text": "Thank you. I've pulled up your order. I can see it was delayed at the courier depot. I'm escalating this as high priority right now." |
| }, |
| { |
| "role": "customer", |
| "text": "How long will an escalation actually take? I've heard that before." |
| }, |
| { |
| "role": "agent", |
| "text": "I completely understand your frustration. I'm contacting the courier directly and will have a confirmed delivery window within 2 hours. I'll also apply a full shipping refund." |
| }, |
| { |
| "role": "customer", |
| "text": "Alright. A 2-hour update is acceptable. What's the reference for this case?" |
| }, |
| { |
| "role": "agent", |
| "text": "Your case reference is TKT-20394. I've also sent you an email confirmation with the courier's direct contact and your shipping refund confirmation." |
| }, |
| { |
| "role": "customer", |
| "text": "I received the email. The refund is noted. I expect the delivery window shortly." |
| }, |
| { |
| "role": "agent", |
| "text": "Absolutely. You'll receive an SMS from the courier within 90 minutes. Is there anything else I can assist with?" |
| }, |
| { |
| "role": "customer", |
| "text": "No. Just make sure it's delivered. Thank you." |
| } |
| ], |
| "resolution": "Order traced to courier depot delay. Case escalated with priority, shipping refund applied, and delivery window confirmed within 2 hours." |
| } |
| |
| How to Use |
| Load with Python (local JSON) |
| python |
| |
| import json |
| |
| with open("multi_domain_support.json", "r", encoding="utf-8") as f: |
| data = json.load(f) |
| conversations = data["conversations"] |
| |
| # Example: print all problem descriptions |
| for conv in conversations: |
| print(conv["domain"], conv["problem"]) |
| |
| Load with Hugging Face Datasets |
| python |
| |
| from datasets import load_dataset |
| |
| dataset = load_dataset("ai-training-datasets/multi-domain-support", split="train") |
| print(dataset[0]) |
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| License |
| |
| This dataset is released under the MIT License. You are free to use, modify, and distribute it for both research and commercial purposes. |
| Citation |
| |
| If you use this dataset in your work, please cite it as: |
| text |
| |
| @dataset{ai_training_datasets_2026, |
| title = {AI Training Datasets: Multi-Domain Support Conversations}, |
| author = {AI Training Datasets}, |
| year = {2026}, |
| version = {1.0}, |
| publisher = {Hugging Face} |
| } |
| |
| ## 💼 Commercial licensing |
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| This dataset is available for free under the MIT License for non-commercial use. |
| If you need a commercial license, custom datasets, or the full version with all 600 conversations, contact me at: |
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| 📧 cybernovasg@gmail.com |
| |
