No matter what kind of conversational AI you are building
Natural language understanding shouldn't be the hard part.
Search, Help Centers, Bots
Self-serve capabilities for the long-tail of customer requests
Analytics that can match a long-tail of intents
Triage and routing
Rules to route voice/text conversations to the right human or resource.
Contextual information (such as smart replies or sales cues)
RPA & process automation
Bots for HR, sales, customer support, business rules and processes
Content moderation and curation
Human-driven workflows, AI superpowers
Tooling for discovering, training and managing intents at scale.
AI-assisted labeling workflows
Labeling conversational data is the first step to building custom NLU models that can support your automation, augmentation or analytics roadmaps.
Discover intents within your data
Knowing what intents exist in your data is the hardest part of getting a conversational AI project off the ground. Our bottom-up workflow promotes iteration and refactoring, allowing teams to identify and train an increasingly long and specific tail of intents.
Build custom, re-usable intent catalogs
HumanFirst promotes re-usability and modularization of training data: our cascading hierarchical organization allows you to train both high-level and low-level intents, and easily use these to accelerate the training and discovery of intents within new training data.
Search your data without CTRL+F
HumanFirst helps teams explore their conversational data with semantic search: instead of relying on keywords, users can provide an example utterance, and HumanFirst immediately surfaces utterances from the entire dataset based on semantic similarity.
Tune & improve your NLU model
Reviewing and fixing labeling issues is critical to improving the performance of your NLU. Our data pipeline runs K-Fold split analysis of the model, identifying problematic training examples and providing an actionable workflow to immediately fix the training data issues.
We've been using Rasa ever since we started LimeChat. The HumanFirst <> Rasa integration allowed us to continuously improve our projects with conversational data generated from our deployed bots. We now ship projects and improve existing ones in record time.
Having been in the IVR space for over 10 years, developing an intent catalog from historical conversational data was painful and required a lot of manual efforts. HumanFirst is introducing an approach and tooling that makes this activity way more efficient and scalable.
CEO, Nu Echo
Having more than 1000 intents with 100+ utterances on average, we were finding it difficult (if not almost impossible!) to manage via Excel. The HumanFirst platform has helped efficiently maintain and organize our intents at scale as well as made it easier for our data annotator to edit them, data scientists to build models on them and provide transparency to our business stakeholders.
Joel Prince Varghese
Staff Data Scientist, eHealth
Features built for scale.
Integrate within your existing workflows and platforms today
From Active Learning to solving the problems of custom data labelling, to fixing misclassification HumanFirst is doing all in a single package, each step at a time.
Software Developer at Citrix
HumanFirst Studio seems to streamline a lot of the development process of a chatbot. I may have a look at getting premium access.
Manager at HolaBrief
I have been wanting something like this since I started working with conversational interfaces. This is something a lot of my previous customers have missed: a way for non-technical people to manage conversational training at scale. Now I have somewhere to send them.