FOR LEAN PRODUCT TEAMS
Agent driven performance
breakthroughs in monthsdays.
Turn your proprietary data into a SOTA model.
No ML team required.
No six-month research hire.
HOW IT WORKS
A model, packaged for your hardware.
You drop the dataset and name the eval. Agents stand themselves up, propose strategies, run the experiments, and hand back the best model.
CASE STUDIES · PUBLISHED BENCHMARKS
breakthroughs in the field.
10,000+ EXPERIMENTS · 280K MEMORIES · CONTINUOUS AGENT-DRIVEN BREAKTHROUGHS
open-tq-metal: fused attention for 70B at 128K
Open-source fused compressed-domain attention on Apple Silicon. Custom Metal kernels compress the KV cache from 40 GB to 12.5 GB, enabling Llama 3.1 70B at 128K context on a single 64 GB Mac - a configuration no other framework can reach.
inference kernel optimization on apple silicon
A coordinated swarm found kernel-level fusions that CoreML doesn't emit, reaching 6.3× faster inference than CoreML on Apple Neural Engine across six generations of Mac hardware.
distributed swarm autoresearch
A swarm of 115 agents collaborated across distributed GPUs, sharing every experiment and every finding through a collective memory network. 3,100 NanoGPT runs, each one compounding on the others.
WHO YOU'RE WORKING WITH
“Your data becomes a moat the day it becomes a model. Your model.”
Austin Baggio
CEO · Co-founder
Former Google, Xero, Mina & NEAR ecosystems
SOLUTIONS
What we optimize.
01 · INFERENCE OPTIMIZATION
Make the model you already have faster.
Your model is fine. It's too slow, or too expensive, or both. A swarm searches the kernel and runtime space across Apple ANE, GPU kernels, quantization schemes, and compilation targets, and finds the speedup a research hire would take months to hunt down.
We make the model you already shipped run dramatically faster on the hardware you need to run it on.
6.3×on Apple Neural Engine
02 · MODEL OPTIMIZATION
Train a better model than the one you have.
Your model's quality isn't good enough yet. A swarm runs thousands of coordinated training experiments across RL, fine-tuning, architecture search, and data curation, converging on a model that meaningfully beats your current baseline.
We don't tune one thing at a time. A swarm finds the combination of changes that a single researcher, working alone, would take six months to stumble into.
0 → 60%win rate, six days
“A single researcher tunes one thing at a time. A swarm tries fourteen approaches before lunch.”
Sai Vegasena
CTO · Co-founder
Former Trail of Bits, Zoom, o1Labs
Deployment · Two paths
On our cloud
Your swarm, our infrastructure. Start the same day, ship the model when it's ready.