Transform unstructured text and conversational data into business and artificial intelligence data.
NLU design allows you to create labeled NLU datasets from your raw text and conversational data, bottom-up.
Increase the coverage and accuracy of your conversational AI on a continuous basis. Discover new intents, improve existing ones with additional training examples and edge-cases from real-life conversations, and prioritize new dialogue flows based on volume of similar requests.
Explore ASR voice data with clustering, semantic similarity (powered by LLMs) and model-assisted search filters. Curate long-tail NLU training datasets for your CCAI use-cases 10-100x faster.
Build, manage and re-use thousands of custom intents and entities across your customer support and contact center channels.
Get rid of spreadsheets and clunky keyword searches. Explore your voice of the customer data, help center tickets, website & helpdesk search queries, social media, emails, product reviews, customer feedback, NPS verbatims etc ... with the swiss-army knife of natural language data.
Easily apply high-dimensionality LLM latent spaces to explore and label nuanced and high-noise unstructured data (like voice data). Prepare fine-tuning datasets for LLMs from your labeled data. Store, explore, categorize and supervise the output of generative models.
Integrate any text or conversational data source via our robust UX, native integrations, CLI and APIs.
Explore unstructured data with full-text search, real-time semantic search, and clustering.
Combine clustering, semantic search, and trained model evaluation metrics (similarity, entropy, margin, uncertainty) to pinpoint the exact data you need.
Convert unstructured data into hierarchies of labeled intents that easily scale to the thousands
Refactor with drag & drop interface to organize, split, merge and move intents & entities
Fast, flexible, stress-free and quality-guaranteed labeling experience.
Seamlessly evaluate NLU performance against multiple models
Generate actionable K-fold split reports for intents and entities
Track evolution of model data with revisions and compare diffs between snapshots
Import data across workspaces with Github-level merge and diff flows
Reuse taxonomies across verticals, sectors or projects.
Export and synchronize your data with NLU, LLM, conversational AI and CCAI providers.
Bring perfectly categorized taxonomies of labeled text and conversational data to your team: provide qualitative and quantitative insights to inform CX priorities and roadmaps.
Track data provenance
APIs & CLI
Flexible data ingestion
Bring your own NLU or LLM
Revisions, diffs, revert, cloning
Advanced user permissions
SSO / SAML
Future-proof your roadmap with an enterprise-ready NLU data hub, pipeline and APIs
Previous intent taxonomy built for loyalty program using manual workflows and a top down approach took months to build and had very bad performance (70% F1 score, 55% accuracy)
Rebuilt entire intent taxonomy using production chat transcripts and utterances in under 2 weeks using HumanFirst Studio. The resulting model was drastically improved (94% F1 score, 89% accuracy)
accuracy in first
gain on model
"In 6 hours of work
with HumanFirst, I’ve
improved our model’s
F1 score by 6%”
“HumanFirst has changed the way we design, build and monitor our bots. As we have millions of conversations every month, monitoring and getting insight into what our customers are really saying was like a needle in the haystack.”
“You are solving one of the biggest pain points
in conversational AI”
"HumanFirst delivers every aspect of what we were looking for and then much more. With it, we are able to build and maintain accurate voice of the customer models that help us drive actionable insights”
“In our initial tests,
HumanFirst yielded 200%+ improvements
in intent coverage,
accuracy over our