Data as of Sep 21, 2026 · Based on 367 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
For developer or research teams that need programmable, reproducible, persona-driven multi-turn dialogues, choose 
Brands AI recommends here
Named in 39% of answers
Named in 38% of answers
Best for developer and research teams needing fine-grained, reproducible, persona-driven multi-turn customer dialogues for model training and evaluation; requires developer skills to run and customize.
Named in 28% of answers
Named in 28% of answers
Source | Citation share |
|---|---|
 Channel-Labs/synthetic-conversation-generation - GitHubhttps://github.com/Channel-Labs/synthetic-conversation-generation | 4% |
Introducing the Synthetic Data Generator - Build Datasets with Natural Languagehttps://huggingface.co/blog/synthetic-data-generator | 4% |
 Best synthetic data generation tools for 2026https://www.k2view.com/blog/best-synthetic-data-generation-tools/ | 3% |
 Open-Source Library to Generate Realistic Synthetic ... - Reddithttps://www.reddit.com/r/LLMDevs/comments/1kavrga/opensource_library_to_generate_realistic/ | 2% |
strova-ai/customer_support_conversations_dataset · Datasets at Hugging Facehttps://huggingface.co/datasets/syncora/customer_support_conversations_dataset | 2% |
 DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applicationshttps://arxiv.org/abs/2409.19020 | 2% |
 aimultiple.comhttps://research.aimultiple.com/synthetic-data-chatbot/ | 2% |
 Top 5 Synthetic Dataset Generators 2026: Rankedhttps://futureagi.com/blogs/top-5-synthetic-dataset-generators-2025 | 2% |
[2506.10622] SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluationhttps://arxiv.org/abs/2506.10622 | 2% |
 Top 6 Synthetic Data Generation Tools [2026]https://averroes.ai/blog/synthetic-data-generation-tools | 1% |
If your goal is specifically **realistic customer conversations**—support chats, sales conversations, complaints, refunds, escalations, etc.—I’d choose based on whether you’re generating conversations **from scratch** or **augmenting an existing corpus**. ### My shortlist Tool | Best fit | Conversation/text generation…
If your goal is specifically realistic customer conversations—support chats, sales conversations, complaints, refunds, escalations, etc.—I’d choose based on whether you’re generating conversations from scratch or augmenting an existing corpus.
| Tool | Best fit | Conversation/text generation | From existing customer data | API / automation |
|---|---|---|---|---|
| Tonic.ai | Enterprise customer-support/chat datasets | Strong | Strong | Strong |
| NVIDIA NeMo Data Designer | Developer-controlled synthetic datasets | Strong | Strong | Strong |
| MOSTLY AI | High-fidelity structured/customer data | Moderate | Strong | Strong |
| SDV / DataCebo | Open-source, Python-heavy workflows | Mostly structured | Strong | Strong |
For customer conversations specifically, Tonic.ai is particularly relevant because its Textual product is designed to synthesize/de-identify unstructured data such as emails, chats, tickets and documents, while its newer Fabricate product can generate realistic unstructured data from scratch.
If you're starting with no real conversations, I wouldn't necessarily use a conventional synthetic-data platform alone. I'd build a scenario → customer persona → conversation simulation → quality filtering pipeline using an LLM.
For example, define:
Scenario:
Customer wants to cancel a subscription
Customer:
- impatient
- 3 years as customer
- believes they were incorrectly charged
- uses informal language
Agent:
- follows support policy
- cannot issue >$100 refund without approval
Conversation:
8–20 turns
realistic interruptions / ambiguity
eventual resolution or escalation
Then generate thousands of conversations while varying:
That approach gives you much more control over behavioral diversity than simply asking a synthetic-data platform to "generate 100,000 customer chats."
If you already have real customer conversations and want synthetic versions that retain their statistical/linguistic characteristics while removing sensitive information, I'd look closely at Tonic Textual. Tonic explicitly supports unstructured datasets including email, chat and tickets.
If you want to generate an entirely new corpus from a specification, Tonic Fabricate is also worth evaluating; its current workflow lets you describe the dataset conversationally and iteratively generate it while preserving specified relationships and constraints.
For a developer-oriented alternative, NVIDIA's current synthetic-data tooling is worth considering; the synthetic-data landscape changed substantially after NVIDIA's acquisition of Gretel.
If you tell me what you're using the conversations for—e.g. fine-tuning an LLM, evaluating a customer-support agent, training a classifier, testing a chatbot, or creating a benchmark—I can narrow this down to the most appropriate tool and architecture.
That approach gives you much more control over behavioral diversity than simply asking a synthetic-data platform to "generate 100,000 customer chats."
If you already have real customer conversations and want synthetic versions that retain their statistical/linguistic characteristics while removing sensitive information, I'd look closely at Tonic Textual. Tonic explicitly supports unstructured datasets including email, chat and tickets.
When generating a synthetic dataset of realistic multi-turn customer conversations, traditional tabular synthetic data tools (like [SDV](https://sdv.dev/) or [Mockaroo](https://mockaroo.com/) ) fall short because they struggle with contextual, multi-turn dialogue…
When generating a synthetic dataset of realistic multi-turn customer conversations, traditional tabular synthetic data tools (like SDV or Mockaroo ) fall short because they struggle with contextual, multi-turn dialogue structures.
Instead, the gold standard for conversational data involves LLM-driven multi-agent simulation frameworks or specialized text synthesis pipelines . The best options depend on your technical setup and scale:
To help narrow down the best tool or script architecture for you, tell me:
Generating a realistic, multi-turn synthetic dataset of customer conversations requires tools that can simulate **distinct user personas**, **domain-specific intents** , and **natural conversation flow/termination** (rather than arbitrary…
Generating a realistic, multi-turn synthetic dataset of customer conversations requires tools that can simulate distinct user personas, domain-specific intents , and natural conversation flow/termination (rather than arbitrary cutoffs).
Because rigid template tools (like traditional Mockaroo or Faker) fail at complex, contextual dialogue, the industry relies on LLM-powered agentic simulation frameworks.
The top approaches and tools available to generate realistic customer service conversations include:
To help narrow down the best path, tell me:
For **realistic customer conversations specifically**, I’d shortlist these: 1. **[tonic.ai](https://www.tonic.ai/products/fabricate?utm_source=chatgpt.com)** — probably the strongest fit if you want end-to-end synthetic customer interactions. It can generate free text such as emails and other unstructured data,…
For realistic customer conversations specifically, I’d shortlist these:
If your dataset looks something like:
customer_id
conversation_id
timestamp
customer_message
agent_message
intent
issue_type
sentiment
resolution
customer_tier
channel
I'd start with Tonic Fabricate if you want a relatively turnkey system. Its current tooling can generate structured data alongside unstructured emails/text and preserve logical relationships between records.
If what you really need is thousands/millions of high-quality multi-turn dialogues for fine-tuning or evaluating a conversational model, I'd also seriously evaluate Gretel, because it specifically supports synthetic unstructured text and LLM-training workflows.
The important distinction is that generic synthetic-tabular-data tools aren't necessarily good conversation generators. Realistic dialogue requires modeling things like turn-taking, escalation, incomplete information, typos, emotional changes, agent mistakes, resolution trajectories, and correlations between the conversation and structured customer attributes.
If you tell me (a) the industry, (b) roughly how many conversations you need, and (c) whether you're using them for LLM fine-tuning, evaluation, or testing, I can give you a much more specific recommendation and architecture.
If your goal is **realistic, multi-turn customer-service conversations**—rather than generic tabular synthetic data—I’d shortlist these: Tool | Best for | My take --- | --- | ---
If your goal is realistic, multi-turn customer-service conversations—rather than generic tabular synthetic data—I’d shortlist these:
| Tool | Best for | My take |
|---|---|---|
| NVIDIA NeMo Data Designer | Programmable, large-scale conversation datasets | Best overall for technical teams |
| SDialog | Persona-driven customer dialogue generation | Best if conversation realism is the priority |
| NVIDIA NeMo Safe Synthesizer | Generating synthetic data from existing customer data | Best when you have real conversations to learn from |
| Custom LLM pipeline | Maximum control over scenarios/personas/evaluations | Best if you have engineering resources |
build.nvidia.com is particularly compelling because it is designed as a compound AI system for synthetic data generation, and NVIDIA provides workflows for generating training-ready SFT chat data. Its synthetic-data tooling supports domain-specific topics, scenarios and personas, and can produce structured conversation datasets.
For customer conversations, I'd structure generation around:
NeMo already has customer-service evaluation scenarios covering things such as billing disputes, refunds, defective products, order delays, outages, account access and plan upgrades, which is a useful indication of the kinds of scenarios you can model.
I'd look harder at NeMo Safe Synthesizer. It can take existing CSV/JSON/JSONL/Parquet/text data, perform PII replacement, fine-tune a model, generate synthetic records, and produce quality/privacy evaluation reports.
That's a fundamentally different approach from simply prompting an LLM:
Real conversations → remove/replace PII → learn distribution → generate new conversations → evaluate similarity + privacy
That generally has a better chance of capturing your company's actual conversational style and weird edge cases.
If you're building this dataset for training/evaluating a customer-service LLM, I would not choose a generic synthetic-data product such as Faker, Mockaroo, or a tabular synthetic-data platform. Those are useful for structured records, but your hard problem is generating coherent multi-turn behavior.
For example, a good generator should be able to produce something like:
Customer starts by asking why their bill increased → agent explains → customer reveals they were promised a different price → agent asks for account details → customer changes the subject to cancellation → agent discovers cancellation fee → customer becomes frustrated → agent offers an alternative → customer accepts. That stateful progression is much more valuable than simply generating 10,000 independent
{customer_message, agent_response}pairs.
If I were building this today:
No existing data: → NeMo Data Designer + an LLM judge/evaluator
Have thousands of historical conversations: → NeMo Safe Synthesizer + evaluation pipeline
Need maximum realism/control: → Build a multi-agent simulator where separate models play customer, agent, and optionally tool/backend, with deterministic scenario state underneath.
For model training, I'd ultimately output something like OpenAI-style messages JSONL; NVIDIA's tooling supports multi-turn chat datasets in this format.
If you tell me what the conversations are for (fine-tuning, evals, chatbot testing, QA, etc.) and roughly how many you need (10K / 100K / 1M+), I can recommend the specific tool/architecture and give you a concrete generation pipeline.
Generating a realistic synthetic dataset of customer conversations is best done using **multi-agent LLM simulation frameworks** rather than static mock data generators (like Faker or Mockaroo), because conversations require context, persona consistency, and multi-turn…
Generating a realistic synthetic dataset of customer conversations is best done using multi-agent LLM simulation frameworks rather than static mock data generators (like Faker or Mockaroo), because conversations require context, persona consistency, and multi-turn realism.
The top tools and approaches available for this task depend on your technical workflow:
idiap/sdialog) : A specialized, open-source Python framework designed explicitly for Synthetic Dialog Generation . It allows you to programmatically spin up simulated customer personas with distinct backgrounds and emotional states, pair them with a custom support agent, and run multi-turn dialog simulations that output directly to structured files (like JSON). It's ideal if you want fine-grained control over local or API-based LLM backends.To help narrow down the best choice for you, could you tell me:
For generating **realistic customer conversation datasets**, the best tool depends on whether you need **training data**, **evaluation/testing data**, **privacy-preserving replicas of real chats**, or **large-scale simulation**. There is no single winner, but these are the strongest options: Tool | Best for |…
For generating realistic customer conversation datasets, the best tool depends on whether you need training data, evaluation/testing data, privacy-preserving replicas of real chats, or large-scale simulation. There is no single winner, but these are the strongest options:
| Tool | Best for | Strengths | Limitations |
|---|---|---|---|
| NVIDIA developer.nvidia.com | Enterprise-scale synthetic conversations | Strong control over personas, scenarios, validation pipelines, and large-scale generation | More engineering effort |
| Tonic.ai tonic.ai | Realistic customer-support datasets | Designed for realistic synthetic data workflows and structured customer data | Commercial product |
| Custom multi-agent LLM pipeline | Maximum realism and control | You can model customer personas, agent policies, escalation paths, emotions, and edge cases | Requires engineering |
| Hugging Face synthetic-data workflows | Fast experimentation | Large ecosystem, easy sharing, many existing datasets/tools | Usually requires more tuning for production quality |
| MOSTLY AI | Learning patterns from existing conversations | Good when you have historical conversations and want privacy-preserving synthetic versions | Less focused specifically on dialogue generation |
DevToolLab NVIDIA Perspectives## My recommendation by use case
Use Tonic.ai Fabricate or MOSTLY AI.
Best workflow:
This generally produces more realistic data than asking an LLM to invent conversations from scratch.
Use a custom multi-agent generation pipeline or NVIDIA NeMo Data Designer.
A good architecture:
Scenario Generator
↓
Customer Persona Agent
↓
Customer Support Agent
↓
Conversation Critic
↓
Quality Filter
↓
Dataset Export
Generate metadata alongside each conversation:
{
"industry": "ecommerce",
"intent": "refund_request",
"customer_sentiment": "frustrated",
"difficulty": "high",
"resolution": "refund_processed",
"turns": 14
}
This makes the dataset useful for training and evaluation.
Consider specialized simulation approaches. Synthetic contact-center dialogue is still a hard problem: research shows that generated conversations can look realistic but may miss subtle behaviors found in real agent-customer interactions, so quality evaluation is important.
For the highest realism, I would not use a single prompt like “generate 100,000 customer chats.” I would use multi-agent simulation + automated quality scoring + a small amount of human review.
If you tell me your target use case (fine-tuning an LLM, chatbot testing, call-center QA, analytics, or replacing unavailable historical data), I can recommend a specific stack and generation recipe.
Generate metadata alongside each conversation:
{
"industry": "ecommerce",
"intent": "refund_request",
"customer_sentiment": "frustrated",
"difficulty": "high",
"resolution": "refund_processed",
"turns": 14
}
This makes the dataset useful for training and evaluation.
Consider specialized simulation approaches. Synthetic contact-center dialogue is still a hard problem: research shows that generated conversations can look realistic but may miss subtle behaviors found in real agent-customer interactions, so quality evaluation is important.
Generating realistic synthetic customer conversations requires moving beyond basic text prompts into **multi-agent persona simulation** , where distinct AI agents play the role of a frustrated/confused customer and a brand-aligned support agent over multi-turn dialogues.[](https://arxiv.org/html/2508.04423v1)…
Generating realistic synthetic customer conversations requires moving beyond basic text prompts into multi-agent persona simulation , where distinct AI agents play the role of a frustrated/confused customer and a brand-aligned support agent over multi-turn dialogues.
The "best" tool depends entirely on your technical workflow and whether you need structured code integration, no-code convenience, or evaluation-driven datasets.
Regardless of the tool you choose, ensure your generation pipeline incorporates:
To help narrow down the ideal approach, tell me:
When generating a synthetic dataset of realistic customer conversations (such as support chats, sales calls, or multi-turn troubleshooting), the "best" tool depends heavily on your technical workflow and whether you are starting from scratch or seeding the generation with real transcripts. Top-tier options range from…
When generating a synthetic dataset of realistic customer conversations (such as support chats, sales calls, or multi-turn troubleshooting), the "best" tool depends heavily on your technical workflow and whether you are starting from scratch or seeding the generation with real transcripts.
Top-tier options range from open-source LLM orchestration frameworks to enterprise data platforms:
To help narrow down the ideal approach, tell me:
If your goal is **realistic, multi-turn customer-support conversations** rather than generic tabular synthetic data, I’d narrow the field considerably. ### My pick: Tonic.ai [tonic.ai](https://www.tonic.ai/?utm_source=chatgpt.com) is probably the strongest **overall choice** if you want production-quality synthetic…
If your goal is realistic, multi-turn customer-support conversations rather than generic tabular synthetic data, I’d narrow the field considerably.
tonic.ai is probably the strongest overall choice if you want production-quality synthetic customer conversations at scale. Its current platform supports generating realistic unstructured data, including text, and can generate data from scratch or model it on existing data. Its newer agentic workflow lets you describe the scenarios you want and have the system generate and validate the resulting data.
For customer-conversation datasets specifically, I'd evaluate these:
| Tool | Best for | My take |
|---|---|---|
| Tonic.ai | Realistic production-like support data | ⭐ Best overall |
| NVIDIA NeMo Data Designer | Developers building custom generation pipelines | ⭐ Best programmable option |
| MOSTLY AI | High-fidelity synthetic data from existing datasets | Excellent, especially if you have real conversation data |
| Custom LLM pipeline | Highly specialized conversations/personas | ⭐ Best flexibility |
| Syncora.ai | Dedicated synthetic customer-support conversations | Worth testing for a turnkey approach |
NVIDIA's NeMo Data Designer is particularly interesting if you have engineers who want to build the generation pipeline themselves. It supports generation from scratch or seed data, dependency-aware generation, validation, and LLM-based quality evaluation.
If you're starting from zero and want something like:
Generate 1 million realistic conversations between customers and support agents for an e-commerce company, covering refunds, angry customers, shipping delays, account issues, ambiguous requests, escalation, policy exceptions, etc. I wouldn't simply feed that prompt to a generic synthetic-data platform.
I'd use a scenario → simulation → evaluation pipeline:
That tends to produce much more useful training data than asking one model to "write realistic customer conversations."
There are already examples of this approach emerging: TicketWorld, for example, generates synthetic customer-service scenarios together with interconnected databases, policy documents, and resolution plans specifically for LLM training/evaluation.
If you tell me your industry, approximate number of conversations (e.g. 100K/1M/10M), whether you have real conversations to seed from, and whether the dataset is for fine-tuning or evaluation, I can recommend the best tool/architecture and give you a concrete generation pipeline.
NVIDIA's NeMo Data Designer is particularly interesting if you have engineers who want to build the generation pipeline themselves. It supports generation from scratch or seed data, dependency-aware generation, validation, and LLM-based quality evaluation.
If you're starting from zero and want something like:
Generate 1 million realistic conversations between customers and support agents for an e-commerce company, covering refunds, angry customers, shipping delays, account issues, ambiguous requests, escalation, policy exceptions, etc. I wouldn't simply feed that prompt to a generic synthetic-data platform.
I'd use a scenario → simulation → evaluation pipeline:
That tends to produce much more useful training data than asking one model to "write realistic customer conversations."
There are already examples of this approach emerging: TicketWorld, for example, generates synthetic customer-service scenarios together with interconnected databases, policy documents, and resolution plans specifically for LLM training/evaluation.
