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Explainable &
Accessible AI
Refine your Data Fuel your Models Run them anywhere
Modern AI models are trained on internet sized datasets, run on supercomputers, and enable content production on an unprecedented scale. At Nomic, we build tools that enable everyone to interact with AI scale datasets and run AI models on consumer computers.
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Interact With Massive Datasets
Atlas enables anyone to instantly structure, visualize, and derive insights from millions of rich unstructured data points—across any modality, any use case.
  • Significantly slash capital and time spent on unstructured data aggregation
  • Explore over 10M+ data points at once to unearth mission critical decisions
  • Deploy with ease directly into your production workflow
  • Unlock a new world of collaboration for technical and non-technical teams
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Run AI Models Anywhere
AI should be open source, transparent, and available to everyone. GPT4All enables anyone to run open source AI on any machine.
  • Run Mistral 7B, LLAMA 2, Nous-Hermes, and 20+ more models
  • Run inference on any machine, no GPU or internet required
  • Accelerate your models on GPUs from NVIDIA, AMD, Apple, and Intel
  • Customize your models with retrieval augmented generation
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“A New Flow State of Data Development”
use case
At smarter sorting we connect retailers, brands, and consumers to better product, chemical, and health data—empowering intelligent and sustainable decision making for everyone.

We use Atlas to visualize and map consumer chemical products. Based on what we see, we make tweaks to our classification methodology which improves the speed and decisiveness of our algorithms. Atlas also helps us communicate to customers what AI does for product classification. When they see THEIR products on the map they learn about their own catalog and gain confidence and excitement in our approach.

Atlas has enabled an entirely new flow state of data development and we were able to get started overnight! The best part of using Atlas is that we can find something new within 5 seconds of opening a map AND get lost exploring the data for hours.
Charlie Vallely
Chief Innovation Officer
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“Atlas is T-SNE on steroids”
Laurense Van Der Maaten
Laurens van der Maaten
First Author of T-SNE
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Enhancing Model Quality
use case
At Hugging Face, we want to bring as much transparency to our training data as possible. Exploring data at scale is a huge challenge and we spend a ton of time on data filtering and quality.

Using Atlas, we found several data and model errors that we didn't previously know about. The visualization made it easy to uncover where these errors existed. After addressing these errors we improved our performance metrics by a ton. We plan to use Atlas much earlier in the process during our next engineering cycle.
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Victor Sanh
Lead ML Scientist
Laurense Van Der Maaten
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“Henceforth, it is the map that precedes the territory” – Jean Baudrillard