I am ready for a long road flight for work with a week- or months-long projects.
Nurdle datasets have trained models that keep billions of users safe online
Justin Davis
Co-Founder and CEO
"Nurdle has been used for 6 years by Spectrum Labs to parse billions of online human interactions.
We've used Nurdle data to moderate content for Riot Games, Grindr, The Meet Group, Together Labs, and other gaming, dating, and social media platforms."
We've used Nurdle data to moderate content for Riot Games, Grindr, The Meet Group, Together Labs, and other gaming, dating, and social media platforms."
100% Privacy-Safe Unstructured Text Datasets
Custom synthetic conversational data & labels with human-level accuracy
Shave months off of your AI development time by iterating models daily instead of waiting weeks for labeled datasets.
Hours not weeks
Costs 50%-90% less
Less Risk. Less Hassle.
Even with clean data, labeling is really expensive. Skip the sourcing, cleaning and labeling time - and save a chunk of money.
Synthetic data is 100% privacy-safe with no regulatory risk. Getting it on demand means data scientists can focus on data science.
Use Cases
Your customers are trying to tell you something
Hidden in your social media feeds, emails, user reviews, live chats and support requests are a goldmine of consumer insights that could change your business.
But using real-world, private conversational data to train an AI to detect user intent — from bank fraud to upsell opportunities — is fraught with regulatory and brand risk.
Nurdle unstructured conversational data is completely synthetic — 100% privacy-safe - and modelled from hundreds of terrabytes of real human conversational data so it’s over 90% accurate. All produced in 1 day, cleaned and custom-labeled for immediate use.
But using real-world, private conversational data to train an AI to detect user intent — from bank fraud to upsell opportunities — is fraught with regulatory and brand risk.
Nurdle unstructured conversational data is completely synthetic — 100% privacy-safe - and modelled from hundreds of terrabytes of real human conversational data so it’s over 90% accurate. All produced in 1 day, cleaned and custom-labeled for immediate use.
RLAIF is faster than RLHF — with no need to label preference pairs
RLHF (Reinforement Learning from Human Feedback) is why ChatGPT talks like ChatGPT and tries to be helpful while trying to avoid breaking the law. It’s the result of millions of human-selected preferences between possible responses. Getting datasets of preference pairs is very expensive... but now new techniques show you can achieve the same results using synthetic data — faster and cheaper.
Nurdle synthetic conversational datasets can be customized to reinforce your values, rules and standards with preference-pair labels or labels based on real-world interactions, such as purchases, positive reviews and click-throughs. All at a fraction of the time and cost of human preference-pair data.
Nurdle synthetic conversational datasets can be customized to reinforce your values, rules and standards with preference-pair labels or labels based on real-world interactions, such as purchases, positive reviews and click-throughs. All at a fraction of the time and cost of human preference-pair data.
Do you trust your chatbot to represent your brand?
Chatbots often are the first point of contact for customers who have buying questions or need assistance from your company. Generative AI chatbots can scale your sales and support... if they can understand what your customers want.
Chatbots that don’t understand user questions because they’re over-fitted to narrow use-cases — or chat differently from the brand your customers love — can do more harm than good.
Fine-tuning with Nurdle conversational datasets quickly improves your chatbot experience. Customize for your brand, industry and products with enough data diversity to cover edge-cases and infrequent questions. So your chatbot sounds like you.
Chatbots that don’t understand user questions because they’re over-fitted to narrow use-cases — or chat differently from the brand your customers love — can do more harm than good.
Fine-tuning with Nurdle conversational datasets quickly improves your chatbot experience. Customize for your brand, industry and products with enough data diversity to cover edge-cases and infrequent questions. So your chatbot sounds like you.
Fix your model with the exact data you’re missing
Low-performance in models is almost always related to data. Over-fitting means your model only answers the narrow band of questions it was trained on. Under-fitting means it’s too vague. Low-prevalence data (like fraud, radicalization or threats) is hard to find, so difficult to train a model to recognize.
Nurdle was born out of the need to detect high-risk, low-prevalence activities for Trust & Safety use cases in some of the worlds largest social media, gaming, dating and messaging platforms. Using very small data samples we can produce the volume and diversity of labeled synthetic data modeled on hundreds of terrabytes of real-world conversations so you can get your model into production.
Nurdle was born out of the need to detect high-risk, low-prevalence activities for Trust & Safety use cases in some of the worlds largest social media, gaming, dating and messaging platforms. Using very small data samples we can produce the volume and diversity of labeled synthetic data modeled on hundreds of terrabytes of real-world conversations so you can get your model into production.
Your customers are trying to tell you something
Hidden in your social media feeds, emails, user reviews, live chats and support requests are a goldmine of consumer insights that could change your business.
But using real-world, private conversational data to train an AI to detect user intent – from bank fraud to upsell opportunities – is fraught with regulatory and brand risk.
Nurdle unstructured conversational data is completely synthetic - 100% privacy-safe - and modeled from hundreds of terabytes of real human conversational data so it’s over 90% accurate. All produced in 1 day, cleaned and custom-labeled for immediate use.
Contact Us
Hidden in your social media feeds, emails, user reviews, live chats and support requests are a goldmine of consumer insights that could change your business.
But using real-world, private conversational data to train an AI to detect user intent – from bank fraud to upsell opportunities – is fraught with regulatory and brand risk.
Nurdle unstructured conversational data is completely synthetic - 100% privacy-safe - and modeled from hundreds of terabytes of real human conversational data so it’s over 90% accurate. All produced in 1 day, cleaned and custom-labeled for immediate use.
Contact Us
RLAIF is faster than RLHF - with no need to label preference pairs
RLHF (Reinforement Learning from Human Feedback) is why ChatGPT talks like ChatGPT and tries to be helpful while trying to avoid breaking the law. It’s the result of millions of human-selected preferences between possible responses. Getting datasets of preference pairs is very expensive... but now new techniques show you can achieve the same results using synthetic data - faster and cheaper.
Nurdle synthetic conversational datasets can be customized to reinforce your values, rules and standards with preference-pair labels or labels based on real-world interactions, such as purchases, positive reviews and click-throughs. All at a fraction of the time and cost of human preference-pair data.
Learn more
RLHF (Reinforement Learning from Human Feedback) is why ChatGPT talks like ChatGPT and tries to be helpful while trying to avoid breaking the law. It’s the result of millions of human-selected preferences between possible responses. Getting datasets of preference pairs is very expensive... but now new techniques show you can achieve the same results using synthetic data - faster and cheaper.
Nurdle synthetic conversational datasets can be customized to reinforce your values, rules and standards with preference-pair labels or labels based on real-world interactions, such as purchases, positive reviews and click-throughs. All at a fraction of the time and cost of human preference-pair data.
Learn more
Do you trust your chatbot to represent your brand?
Chatbots often are the first point of contact for customers who have buying questions or need assistance from your company. Generative AI chatbots can scale your sales and support... if they can understand what your customers want.
Chatbots that don’t understand user questions because they’re over-fitted to narrow use-cases — or chat differently from the brand your customers love — can do more harm than good.
Fine-tuning with Nurdle conversational datasets quickly improves your chatbot experience. Customize for your brand, industry and products with enough data diversity to cover edge-cases and infrequent questions. So your chatbot sounds like you.
Learn more
Chatbots often are the first point of contact for customers who have buying questions or need assistance from your company. Generative AI chatbots can scale your sales and support... if they can understand what your customers want.
Chatbots that don’t understand user questions because they’re over-fitted to narrow use-cases — or chat differently from the brand your customers love — can do more harm than good.
Fine-tuning with Nurdle conversational datasets quickly improves your chatbot experience. Customize for your brand, industry and products with enough data diversity to cover edge-cases and infrequent questions. So your chatbot sounds like you.
Learn more
Fix your model with the exact data you’re missing
Low-performance in models is almost always related to data. Over-fitting means your model only answers the narrow band of questions it was trained on. Under-fitting means it’s too vague. Low-prevalence data (like fraud, radicalization or threats) is hard to find, so difficult to train a model to recognize.
Nurdle was born out of the need to detect high-risk, low-prevalence activities for Trust & Safety use cases in some of the worlds largest social media, gaming, dating and messaging platforms. Using very small data samples we can produce the volume and diversity of labeled synthetic data modeled on hundreds of terrabytes of real-world conversations so you can get your model into production.
Contact Us
Low-performance in models is almost always related to data. Over-fitting means your model only answers the narrow band of questions it was trained on. Under-fitting means it’s too vague. Low-prevalence data (like fraud, radicalization or threats) is hard to find, so difficult to train a model to recognize.
Nurdle was born out of the need to detect high-risk, low-prevalence activities for Trust & Safety use cases in some of the worlds largest social media, gaming, dating and messaging platforms. Using very small data samples we can produce the volume and diversity of labeled synthetic data modeled on hundreds of terrabytes of real-world conversations so you can get your model into production.
Contact Us
What’s the difference between real data, synthetic data and Nurdle data?
Real data is taken from the real world and is the best data out there… But it costs 300x as much as synthetic data and takes a very long time to acquire and label, which can slow AI projects to a crawl. And if you’re in a regulated industry, forget about using real user data altogether.
Synthetic data is cheap and fast, but doesn’t improve model accuracy since it’s low-quality and usually is just a bunch of random text that has no connection to the intended use cases of most projects.
Nurdle data is created by using a kernel of real data (yours or ours) and augmenting it using the NurdleGPT unstructured text generator LLM. We produce unstructured text that performs at 92% accuracy of human-generated, human-labeled data at a fraction of the cost and time of curating, prepping and labeling it.
Synthetic data is cheap and fast, but doesn’t improve model accuracy since it’s low-quality and usually is just a bunch of random text that has no connection to the intended use cases of most projects.
Nurdle data is created by using a kernel of real data (yours or ours) and augmenting it using the NurdleGPT unstructured text generator LLM. We produce unstructured text that performs at 92% accuracy of human-generated, human-labeled data at a fraction of the cost and time of curating, prepping and labeling it.
Want to learn more?
See the Methodology on our Fine-Tuning Data page
See the Methodology on our Fine-Tuning Data page
Test your data now for free
Free data test tool you can run without sharing your data shows you clusters, data bias, label skew and likely areas of model failure in your dataset.
Better data makes better models. Faster data means less data science time.
Nurdle cuts data science time by 5x - 10x and costs 50%-80% less than human-labeled data for similar performance. Let Nurdle do it for you.
Free Data Assessment Test
Data Sourcing, Cleaning, Labeling, Prep
Data Gap Analysis Report
Model Monitoring
Custom Lookalike Data
Testing Datasets
Seeing the label bias, data skew and natural clustering of your data can save data scientists hours (or days) trying to figure out what data they need to improve their models.
Get the tool for free here and check it out yourself!
Get the tool for free here and check it out yourself!
Stuck in the cold-start without data to get going? Or looking for data that contains low-prevalence behaviors or content? Or do you just need a bunch of random docs and content turned into usable, labeled datasets (but don’t want to pay human-labeling prices)?
Let Nurdle do it for you. You’ve got better things to do.
Let Nurdle do it for you. You’ve got better things to do.
Nurdle will test your models to figure out what data you need, then curate relevant datasets for you so you don't have to spend weeks doing it yourself.
Want to try it out? Send us a data sample, and we'll send you a free analysis within XYZ days.
Want to try it out? Send us a data sample, and we'll send you a free analysis within XYZ days.
Nurdle will monitor and maintain your AI model to ensure it remains accurate over time.
Declining performance ("model drift") is common with LLMs as words and slang change meaning or go out of style. Data scientists hate the boring job of maintaining models that they've already built, but Nurdle can do it for you and let your data science team focus on building their next big project.
Declining performance ("model drift") is common with LLMs as words and slang change meaning or go out of style. Data scientists hate the boring job of maintaining models that they've already built, but Nurdle can do it for you and let your data science team focus on building their next big project.
We use a kernel of real data to build augmented synthetic datasets that perform comparably to human-labeled data – but are created at a fraction of the price, time, and data scientist time.
All Nurdle data is compliant with privacy regulations
and tailored to your specifc use-case.
All Nurdle data is compliant with privacy regulations
and tailored to your specifc use-case.
Nurdle will create synthetic test datasets that mirror real-world interactions, which data scientists can use to gauge the quality of their models.
Our testing datasets are especially useful and valuable for healthcare, legal, government, and other industries where it's illegal to use real customer data to train AI models.
Our testing datasets are especially useful and valuable for healthcare, legal, government, and other industries where it's illegal to use real customer data to train AI models.
Coming soon
Nurdle Blog
Bringing technology leaders solutions to LLM, Generative AI, and data challenges through product updates, features, and tips.
LLMs
How to Choose a Model on Hugging Face: 5-Step Guide
How to efficiently identify models in Hugging Face for your data science workflow.
Get the most out of Hugging Face with our 5-step guide to model selection. Improve your data analysis today.
Get the most out of Hugging Face with our 5-step guide to model selection. Improve your data analysis today.
Read Article
Nina Lopatina
Reading time: 15 min
01.25.2024
Data for AI
Data Training Strategies for AI Models: Real vs. Synthetic Data, and Repositories
Data is the Michael’s Secret Stuff of artificial intelligence. For any AI project, training and fine-tuning your large language model (LLM) requires essential data to slam-dunk its tasks...
Read Article
Hetal Bhatt
Reading time: 10 min
01.11.2024
LLMs
Enhancing and Fine Tuning Large Language Models (LLMs)
Numerous businesses are excited to release generative AI-powered applications that can provide benefits to both their employees and customers. Thanks to the widespread availability of large language models (LLMs), large language models (LLMs) has opened the doors for innovation — but with a major caveat.
Read Article
Hetal Bhatt
Reading time: 13 min
10.19.2023
Pilot Program
Join Nurdle’s free pilot program to get better data for your AI projects
If you’re reading this, you probably have an AI project that you want to launch or improve. And lucky you – we're looking for a few AI partners to participate in our free (!!!) pilot program so we can show the world what we can do.
Read Article
Hetal Bhatt
Reading time: 7 min
10.18.2023
Meet with one of our data experts to unlock Nurdle's scalability for data creation, preparation, and measurement
Contact Our Team
