Design next-level NLU
Transform unstructured text and conversational data into business and artificial intelligence data.
With
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NLU design
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With
Trusted by Large Enterprises Worldwide
"The tool that turned conversation designers, into NLU designers"
★★★★★ Capterra
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Top Global
Banks
UK Online Directory
Multinational
Pharmaceutical
Large Online Retailer
Top U.S
Telco
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The complete no-code, data-centric
NLU design suite
NLU design allows you to create labeled NLU datasets from your raw text and conversational data, bottom-up.
NLU design for Conversational AI
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.
NLU design for CCAI
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.
NLU design for CX
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.
NLU design for Large Language Models
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.
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Centralize all your unstructured text & conversational data
Integrate any text or conversational data source via our robust UX, native integrations, CLI and APIs.
From exploration to production with
10-100x efficiency
From exploration to production with
10-100x efficiency
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
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Import data across workspaces with Github-level merge and diff flows
Reuse taxonomies across verticals, sectors or projects.
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Apply resulting data to business and artificial intelligence
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.
Let NLU inform your decisions and
accelerate your digital transformation
The Enterprise-Ready solution
No-code enviroment
Collaborative workspaces
Track data provenance
Multi-tenant
Developer-friendly
APIs & CLI
Flexible data ingestion
Bring your own NLU or LLM
Revisions, diffs, revert, cloning
Advanced user permissions
SSO / SAML
Horizontally scalable
On-prem deployment
Future-proof your roadmap with an enterprise-ready NLU data hub, pipeline and APIs
Case Study
Woolworths Group
Problem
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)
Solution
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)
94%
production
F1 score
89%
accuracy in first
iteration
10X
productivity
gain on model
curation
