Identifying what intents a chatbot or virtual assistant needs to support is often the initial analysis step of any project. HumanFirst allows non-technical teams to do this faster, more efficiently, and with higher transparency.
Build trust with customers thanks to a streamlined workflow that provides transparency into the labeling and training process.
Modularizing your intent training data makes it easy to reuse across projects, reducing development time and cost and leading to higher quality and accuracy of NLU.
HumanFirst provides a collaborative centralized hub to build and maintain ground truth for your data.
HumanFirst’s ML-assisted workflows make building datasets easy and collaborative.
Start from raw conversational data with bottom-up labeling, or import noisy labeled data and quickly clean it.
Training shouldn’t stop once a chatbot is deployed: continuously improving the coverage and accuracy of a chatbot with real-life data is the difference between a so-so experience and a great one.
Show customers how easy it is to improve a deployed model, and easily convince them to invest in post-deployment services.
LimeChat was looking for a better labeling solution, as well as an alternative to Excel to centralize their projects’ NLU data and develop re-usable catalogs of intent classifiers.
Since implementing HumanFirst in their workflow, LimeChat accelerated its conversation-driven development by 8.5x.
“We’ve been using Rasa ever since we started LimeChat. HumanFirst’s integration with Rasa has allowed us to continuously improve our Rasa projects with conversational data generated from our deployed bots. Thanks to HumanFirst’s active-learning workflows and collaborative solution, we can now ship new projects and improve existing ones in record time.”
CEO @ LimeChat
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