Collect, Label and Validate Text-Based Training Data

People express ideas and intent in different ways creating a complex job for your Natural Language engines.  Our text-based AI training data provides high quality datasets in multiple languages and domains to improve your NLU, NLG or TTS engines.

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Conversational AI Text Collection

Text datasets consisting of conversations between 2 entities

Text Variant Collection

Datasets consisting of text variants around a specific concept

Text Validation

Validates the quality of any text based dataset on specified criteria

Named Entity Tagging for NER

Annotate and classify single entities in a sentence into pre-defined categories.

Multiple Named-Entity Tagging

Annotate and classify multiple entities in a sentence into pre-defined categories

Sentiment Tagging

Annotate sentences for sentiment, ex. good, neutral, bad

Semantic Annotation

Annotate and classify sentences or phrases by domain and intent

Text Quality Guarantee

Natural language systems rely on multiple quality metrics to function optimally.   We combine Word Error Rate (WER) measurements with our ML algorithms and human in the loop validations to ensure your models operate at maximum accuracy.

Spelling and Grammar

Checks for proper syntax for each language

Inter-Annotator Agreements

F1 score >.8 for all annotations with dynamic judgment utilized to perform tiebreakers

Word Error Rate

Guaranteed <5%


Ensures the datasets use native speakers for transcriptions

Success Stories

For this project, Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment

Keeping a nation’s lights on means constantly inspecting electricity poles for damage. Before EDP partnered with

With the rise of voice technology, this leading global provider of audio equipment wanted to develop an automatic speech recognition (ASR) model

When a global electronics maker came to DefinedCrowd with the goal of building more inclusive facial-recognition

Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine

In the arms race for speech-enabled technologies, systems that can support the widest user base will win out

Visionary companies like Amazon are leveraging sentiment analysis models to dig beyond surface-level understandings