Decide what to train, where to send it, and what it should cost
We do not run the training. We run the layer above it — which workloads have earned a job, which deployed models have drifted, and which need no training at all because a routing change is cheaper.
102,860 outcome-labeled traces under management · 6 workloads · 4 providers connected
How Arcline sits above the trainer
We own the corpus, not the cluster
Every session resolves to shipped, rejected, or unresolved against your merges and tickets. That labeled record is the scarce asset — GPUs are a commodity you can rent anywhere.
Provider-agnostic by design
Applied Compute for RL environments, Prime Intellect for distillation, Together for fast LoRA, or your own trainer air-gapped. We route to fit, not to a vendor deal.
Every checkpoint is verified after the fact
A finished run is a hypothesis until live outcomes confirm it. Checkpoints that regress ship rate get rolled back automatically and stay on the ledger.
Corpus readiness by workload
Verified outcomes vs. the minimum to train responsibly