Summarize and analyze conversations at scale and train bots on high-quality, real-customer data.
Override certain user queries in your RAG chatbot by discovering and training specific intents to be handled with transactional flows.
Design, organize, and save subsets of your data to hone in on key issues. Ingest new data and automate annotation as conversations scale.
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.
Make the most of call recordings and bot logs. Understand your users’ problems in the language they use to express them.
Find gaps, test solutions, and fix hallucinations without manual monitoring or expensive and error-prone labeling.
Use prompts to analyze thousands of conversations at once. Speed through topic modeling and improve model coverage.
Test AI performance on real conversations in a playground environment. Monitor data after deployment to measure success.
Leverage LLMs for full conversational analysis. Quickly group conversations by key issues and isolate clusters as training data.
Automate and oversee intent classifications before you build. Agree on ground-truths with your LLM and test against source conversations.
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.