Design-first exploration

Navigate the invisible architecture of AI

Every foundation model thinks in latent space. Manifold lets you see it, walk through it, and understand it, without flattening it into a scatter plot.

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N-dimensional, not 2D

Slice, rotate, and traverse dimensions interactively. No more collapsing a 768-dim space into a flat map and pretending you understand it.

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Design-first interface

Built for exploration and intuition. The visual language of the tool matches the complexity of the space. Not another matplotlib wrapper.

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Browser-native

WebGPU-powered rendering. Share a link, explore together. No installs, no notebooks, no environment setup.

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Any embedding

Upload from OpenAI, Cohere, custom VAEs, diffusion models, word2vec, CLIP. If it has vectors, Manifold can navigate it.

The latent space is the most important space in AI that nobody can see

Every AI model encodes knowledge into high-dimensional geometry. Clusters, boundaries, gradients, voids. This structure determines what the model knows and how it fails. Today, the best tool for exploring it is a 2D scatter plot and a prayer. We think that's absurd.

Why now

Embedding-based systems are the default architecture of modern AI. The teams building them deserve tools as good as the models they're debugging.

RAG pipelines, semantic search, recommendation engines, generative models. They all run on embeddings. The practitioners building these systems have grown from a handful of ML researchers to millions of engineers, designers, and artists. The tooling hasn't kept up.

Explore the space between the dimensions

Manifold is being built for the people who need to understand what AI actually learned, not just what it outputs.