The fastest path to conversational AI.

Explore, annotate, and operationalize conversational data to test and train chatbots, IVR, voicebots, and more.

Trusted by Enterprise Clients
Trusted by enterprise clients

Still using spreadsheets?

No more manual data discovery, design, or development. There's a better way to build beautiful conversations.

Index, label, and cluster with data automation

Summarize and analyze conversations at scale and train bots on high-quality, real-customer data.

Data automation

Improve model coverage with streamlined discovery and design

Override certain user queries in your RAG chatbot by discovering and training specific intents to be handled with transactional flows.

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Launch and iterate faster
with dynamic datasets

Design, organize, and save subsets of your data to hone in on key issues. Ingest new data and automate annotation as conversations scale.

Dynamic datatsets

Reduce training cost with
synthetic data generation

Generate new data that reflects the behavior of your users to to test and train your models on relevant, non-sensitive data.

Synthetic data generation

AI you can trust for conversations that matter.

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Build from truth

Make the most of call recordings and bot logs. Understand your users’ problems in the language they use to express them.

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Reduce cost

Find gaps, test solutions, and fix hallucinations without manual monitoring or expensive and error-prone labeling.


Accelerate time to market

Use prompts to analyze thousands of conversations at once. Speed through topic modeling and improve model coverage.

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Backtest performance

Test AI performance on real conversations in a playground environment. Monitor data after deployment to measure success.

Data-Driven, Human-First

Build from ground-truth data
with the model of your choice.

Build beautiful conversations with better NLU design.

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Leverage LLMs for full conversational analysis. Quickly group conversations by key issues and isolate clusters as training data.

Intent modeling

Intent modeling

Automate and oversee intent classifications before you build. Agree on ground-truths with your LLM and test against source conversations.

Matching itents
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Fallback analysis

Hone in on what’s not working. Apply prompts to summarize fallback interactions to quickly find gaps and build new capabilities.

Extract sentiment analysis

Let your data drive.