Artificial Intelligence.
A pile of weighted matrices that decided to be useful.
A neural network is a function. You hand it numbers; it hands you back numbers. The middle is a stack of layers — each takes the previous output, multiplies it by a matrix of learned weights, applies a non-linear bend, and passes it forward. Stack enough, and the function gets smart enough to recognize a face, write a paragraph, or steer a wheel.
ACT RELU
FWD LIVE
A stack of linear algebra with attitude.
Imagine a long pipeline. Numbers go in one end. At each station along the way, the numbers get multiplied by a fixed grid of weights, summed, then bent through a non-linear function (the "activation"). The output of one station becomes the input of the next. Eventually a small handful of numbers fall out the other end — the answer.
The miracle isn't the architecture. The miracle is training. You feed the network thousands of examples, measure how wrong its guesses are, and nudge every weight a tiny bit in the direction that would have been less wrong (this is gradient descent). Repeat until the loss flattens out. What's left is a function that — for reasons that humble the people who built it — generalizes beyond the data it saw.
Different shapes of network solve different problems. CNNs see images by sliding small filters across them. Transformers attend to language by relating every word to every other word. Diffusion models generate images by reversing a noise process. Same underlying machinery — different geometry on top.
From input to prediction.
Each step is a deterministic matrix operation. The whole pass takes microseconds on a GPU.
Models, frameworks, runtimes.
Different problems, different shapes of network. Different deployment targets, different runtimes.
Where we've shipped AI.
AI you can poke at right now.
First card runs a real CV model on this site. Other slots are for shipping cases.
Tell us what's brittle. We'll pick the model.
Don't ask "do you have AI." Ask "what would AI actually solve here." Tell us the bottleneck — recognition, generation, decision — and we'll come back with the right model and the right deployment within one business day.