Lab builds post-trained and fine-tuned models on your firm's data, running on infrastructure you control.
01 · Security. Post-trained models run inside your perimeter, on compute you control. Your data never leaves your boundary. No provider retention, no derived data, no aggregate telemetry. The model is yours.
02 · Cost. Frontier inference scales with use. Post-trained models on your own data outperform frontier on your specific workflows at a fraction of the cost, decoupled from any model vendor.
Inference cost per resolved task vs. weeks, relative to the frontier-API baseline (1.00×). The two cliffs are the distilled 32B and 8B tiers taking over their verified workflow shares.
01 · Corpus. Atlas captures corrections, rejections, and accepted outputs from your experts. Lab professionals canonicalize them into governed training examples, separating firm policy from personal preference, attaching authority and provenance.
02 · Evaluation. Before any model is trained, the Lab builds an eval set from the aggregated corpus against which to compare any fine-tuned or post-trained model.
03 · Training. Open-weight models are fine-tuned or post-trained on your corpus, on compute you control or designated. The weights stay in your perimeter.
04 · Routing. Atlas routes each task to the best model on evidence: frontier for hard judgment, your post-trained model for routine work. The mix shifts as the corpus grows.
The trained weights, the corpus, the eval set, and the routing table. All of it portable. If you change providers, change compute, or change your mind, the asset moves with you.