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+# Multi-Domain Support Conversations Dataset
+
+This dataset combines **600 realistic customer support conversations** from six different domains:
+
+- **Customer Service** (100 conversations)
+- **E-commerce** (100 conversations)
+- **Financial Support** (100 conversations)
+- **HR / Onboarding** (100 conversations)
+- **Medical Helpdesk** (100 conversations)
+- **Technical Support** (100 conversations)
+
+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.
+
+## Dataset Structure
+
+Each conversation is stored as a JSON object with the following fields:
+
+| 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 |
+
+## Customer Personas (with descriptions)
+
+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):
+
+- **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.
+
+This balanced distribution ensures your model learns to handle a wide range of communication styles and needs.
+
+## Example Conversation
+
+Below is a sample conversation from the **customer service** domain (customer type: impatient executive):
+
+```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])
+
+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}
+}