The fastest path to conversational AI.
Explore, annotate, and operationalize conversational data to test and train chatbots, IVR, voicebots, and more.
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.
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.
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.
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.
AI you can trust for conversations that matter.
Build from truth
Make the most of call recordings and bot logs. Understand your users’ problems in the language they use to express them.
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.
Backtest performance
Test AI performance on real conversations in a playground environment. Monitor data after deployment to measure success.
Build from ground-truth data with the model of your choice.
Build beautiful conversations with better NLU design.
Clustering
Leverage LLMs for full conversational analysis. Quickly group conversations by key issues and isolate clusters as training data.
Intent modeling
Automate and oversee intent classifications before you build. Agree on ground-truths with your LLM and test against source conversations.
Fallback analysis
Hone in on what’s not working. Apply prompts to summarize fallback interactions to quickly find gaps and build new capabilities.
Building an intent classification around customer loyalty was a manual process. Workflows that took a top down approach and months to build ended up delivering undesired results.
With HumanFirst, Woolworths group rebuilt entire intent taxonomy using production chat transcripts and utterances in under 2 weeks.
