Gretel holds a clear lead for synthetic dialogue data, frequently recommended for producing realistic conversational records without privacy risks. For generating realistic customer conversations specifically, DiaSynth is the usual answer.
For generating a synthetic dataset of realistic customer conversations, my top recommendation is Tonic Fabricate if you want a ready-made tool, especially for customer support chats, emails, and tickets. It can generate data from scratch or use existing data patterns to create more coherent, production-like datasets.
The best choice depends on whether you're generating conversations from scratch, learning from existing transcripts, or building a large dataset for fine-tuning an LLM.
Best tools to shortlist
1. Tonic Fabricate — Best overall starting point
(Top pick)
Best for realistic, interconnected customer conversations and associated customer or order records. Supports generation from scratch and from existing data patterns, with validation features.
Best for: support teams, enterprise datasets, testing, and AI training.
I need to create a synthetic dataset that mimics realistic customer support chats. Which tools are recommended for generating this kind of dialogue data?
2. NVIDIA NeMo Data Designer — Best for code-first generation at scale
A Python-based framework for building configurable synthetic-data pipelines, combining language models, structured generation, and validation. A strong option if you want to programmatically generate large volumes of training examples.
Best for: ML engineers, fine-tuning pipelines, custom generation logic.
Build repeatable generation and evaluation workflows using LLMs. Useful for generating multi-turn dialogues, customer personas, and labeled training examples, with flexibility to customize the pipeline.
Best for: developers who want control over models, prompts, and cost.
Worth considering when you already have customer data and need synthetic datasets that preserve useful characteristics of the source. Validate its current support for your specific multi-turn conversation format before choosing it.
Best for: privacy-sensitive data projects and data sharing.
Coverage: different customer intents, products, customer profiles, conversation lengths, and difficulty levels.
Quality controls: schema validation, duplicate detection, policy correctness, and human review of a sample.
One important caveat: synthetic transcripts can sound convincing while still failing to represent real customer behavior. A 2026 research paper on contact-center dialogue generation highlights the importance of measuring realism and downstream task performance, rather than judging transcripts by fluency alone.
The "best" tool for generating synthetic customer conversations depends on whether you prefer a code-first framework (for deep customization and scale) or an LLM evaluation toolkit (for out-of-the-box multi-turn generation).
The top options break down by use case:
DeepEval (by Confident AI): Best overall evaluation-driven synthesizer. Its Synthesizer class allows you to feed in seed data, specify a domain (e.g., e-commerce, banking), and automatically generate realistic, multi-turn customer-agent dialogue datasets optimized for testing and fine-tuning.
RAGAS (Retrieval Augmented Generation Assessment): Excellent if your goal is building customer support bots or RAG pipelines. RAGAS includes a robust test-dataset generator that can synthesize diverse customer queries and multi-turn conversational interactions from your source documentation.[](https://www.kaggle.com/datasets/syncoraai/customer-support-conversations)
Custom LLM-Agent Frameworks (LangChain / LlamaIndex): If you need heavily nuanced personas (e.g., an angry telecom customer vs. an empathetic support rep), writing a custom Python loop using GPT-4o or Claude 3.5 Sonnet with persona prompting gives you absolute control over sentiment, intent, and resolution states.
Open-Source Repositories (e.g., Channel-Labs synthetic-conversation-generation): Specialized community GitHub libraries automate user persona creation, dynamic multi-turn dialogue simulation, and realistic conversation termination (e.g., detecting when a customer gets frustrated or resolved).[](https://github.com/Channel-Labs/synthetic-conversation-generation)
To help narrow down the ideal path, tell me:
What is the primary use case for the dataset? (e.g., fine-tuning an LLM, evaluating a chatbot, or analyzing agent QA scorecards)
Do you prefer a Python coding workflow or a no-code/low-code platform?
To generate authentic synthetic customer chat dialogues, several software libraries, platforms, and frameworks specialize in structuring, scaling, and varying conversational data.
Here are the top options categorized by how they handle the generation process:
No-Code & GUI-Based Generators
Hugging Face Synthetic Data Generator : Powered by distilabel , this tool allows you to build custom chat datasets using natural language prompts without writing code. It is designed for multi-turn supervised fine-tuning and mirroring real-world distributions.
Distilabel by Argilla : An open-source framework specifically built for synthetic data generation and LLM alignment. It features robust pipelines to create instruction-following and conversational multi-turn dialogues with custom personas.
Ragas (Retrieval Augmented Generation Assessment) : While largely used for testset generation, Ragas includes scenario and conversation simulation capabilities that can generate diverse user-query and agent-response test matrices from seed documents.
LangChain Testset Generator : A programmatic approach to leverage LLMs like OpenAI or open-source models to fabricate realistic interaction data based on your specific company knowledge base or documentation templates.[]()
Custom Prompting & Orchestration (LLM APIs)
OpenAI / Anthropic APIs via Python: Writing a custom script using structured outputs (Pydantic models in Python) to force an LLM to output dialogues with specific variables (e.g., customer frustration level=8 10 equals eight-tenths=8 1 0 , product type = "SaaS subscription", and tone = "confused").
If you'd like, let me know:
Do you prefer a no-code GUI tool or a Python coding framework?
What specific industries or scenarios (e.g., retail refunds, technical troubleshooting) do you need to simulate?
I can provide a tailored workflow or a sample prompt structure for your project.
If you’re building a dataset of fake but realistic customer support chats, look for tools that can control personas, intents, conversation flows, and quality filtering—not just generate random text. Here are some good options:
Tool
Best for
Notes
OpenAI API + custom prompting
Flexible, general-purpose generation
You can create customer personas, support scenarios, escalation patterns, and structured JSON outputs. Good when you want full control over schema and scale.
For realistic customer-support chat datasets, I’d recommend these options:
Tool
Best for
Why
NVIDIA NeMo Data Designer
Best overall for production-scale datasets
Designed specifically for synthetic-data pipelines; supports multi-turn chats, personas, scenarios, structured fields, validation, and reproducible generation. It can also generate tool-calling conversations and DPO pairs.
Hugging Face Synthetic Data Generator / Distilabel
Open-source experimentation
Supports generating chat datasets from prompts and is built around the open-source Distilabel ecosystem.
Argilla + LLMs/LangChain
Generation plus human review/labeling
Useful when you want humans to inspect, annotate, and refine generated customer-support examples rather than blindly accepting generations.
Direct LLM API + your own generator
Maximum flexibility
Best when you need very specific conversation logic, e.g. simulated customers, agents, policies, escalation rules, CRM/tool calls, or proprietary schemas.
My pick: NeMo Data Designer
For your particular use case, NeMo Data Designer is especially interesting because NVIDIA provides an explicit multi-turn chat recipe that varies conversation length, complexity, and user mood and includes LLM-based evaluation.
It also supports a customer-support-specific workflow for generating tool-calling SFT conversations, which is valuable if your eventual model needs to simulate actions such as:
Customer: My package hasn't arrived.
Agent: Let me check that for you.
→ get_order(order_id="12345")
Tool: Status = "Delayed", ETA = "Oct 8"
Agent: Your package is delayed and is now expected October 8.
NVIDIA's synthetic-data documentation explicitly lists customer-support tool conversations as a supported use case.
For realism, don't just generate random conversations
I'd structure the dataset around scenario dimensions such as:
edge cases: contradictory information, missing order number, repeated requests
resolution: solved, unresolved, transferred to human
Then use an LLM to generate the actual natural-language dialogue conditioned on those variables, rather than asking:
"Generate 100 customer-support conversations."
That approach gives you much better control over coverage and distribution. Data Designer specifically supports dependency-aware generation, validation, and LLM-as-a-judge evaluation, which is useful for this kind of structured dataset design.
Recommended stack:NeMo Data Designer + a strong LLM for generation + an independent LLM judge + a small manually reviewed validation set.
If you tell me whether you're building this dataset for fine-tuning, evaluation, or training a customer-support agent, I can also give you a concrete schema and generation pipeline (including example prompts and JSONL format).
Creating realistic customer support chat logs requires generating multi-turn, goal-oriented dialogues with distinct personas (an annoyed/confused customer and a polite/helpful support agent), varying intents, and domain-specific knowledge.[](https://www.evidentlyai.com/blog/synthetic-data-generator-python)
Argilla / Distilabel: is an open-source AI-native data generation framework built for fine-tuning and alignment. It allows you to build custom multi-stage pipelines where you can script dual-agent roleplay (Customer Agent↔left-right arrow↔ Support Agent) conditioned on a knowledge base or product manual to output structured, multi-turn dialogue datasets.
4. Quality filtering
A realistic pipeline usually:
Generate scenarios
Generate conversations
Score for realism, consistency, and policy compliance
Keep only high-quality examples
A similar multi-stage approach is described in synthetic customer-support generation workflows that first create scenarios, then conversations, then filter results.
My shortlist by use case
Need 10k–1M chats quickly: LLM API + your own generation pipeline.
Need labeled training data: StrataSynth or a structured simulator.
Need open-source/self-hosted: SDialog or NeuralWOZ.
Need sales/customer role-play rather than support: Customer-simulation models such as CustomerLM-style approaches are designed specifically to keep the “customer” role in multi-turn conversations.
If you tell me your target use case (fine-tuning a chatbot, analytics testing, intent classification, QA testing, etc.) and desired dataset size, I can narrow this down to a recommended stack.
DeepEval (by Confident AI): DeepEval features a built-in Synthetic Data Generator specifically designed for RAG and conversational applications. You can feed it your documentation, FAQs, or support articles, and it will automatically generate realistic customer personas, edge-case queries, and complete multi-turn conversational trajectories.
Custom Multi-Agent Simulations (LangGraph / AutoGen) : If you need hyper-specific control over customer behaviors (e.g., simulating a frustrated customer who changes their mind mid-chat, or an agent who has to query an external API), building a custom two-agent loop using LangGraph or Microsoft AutoGen is a powerful approach. One agent is prompted with a specific customer persona and a hidden "goal," while the second agent is constrained by your company's support guidelines. They converse until the issue is resolved or escalated.[](https://aclanthology.org/2023.gem-1.16/)
Tonic Fabricate : For enterprise-grade workflows requiring structured relational integrity alongside generated context, Tonic Fabricate uses an agentic approach to model conversational and tabular data patterns based on your production configurations or precise text prompts.[](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)
Core Best Practices for Dialogue Generation
Condition on a Knowledge Base: Don't let the LLM hallucinate company policies from scratch. Provide a snippet of a real FAQ, policy doc, or product manual during generation so the support agent's responses remain grounded.
Inject Persona Profiles: Pre-define metadata parameters for each chat session before triggering generation:
Customer Sentiment: Frustrated, confused, neutral, or in a rush.
Issue Type: Technical bug, billing dispute, return request, etc.
Resolution Path: Successfully resolved, escalated to human, or abandoned.
Use "LLM-as-a-Judge" for Filtering: Pass your generated chats through a secondary evaluation script to drop low-quality outputs (e.g., where the agent breaks character, or the conversation ends illogically after one turn).
If you'd like to share, let me know:
The specific domain or industry (e.g., SaaS, e-commerce, fintech)
Whether you prefer a Python-based open-source library or a managed platform
I can help you outline a custom prompt architecture or pipeline for your setup.
