AutoSmith runs AI agents that fine-tune custom models end-to-end. Describe what you need in plain English. Agents handle data, training, evaluation, and iteration. No ML engineers required.
Tell AutoSmith what you need in plain English. "Classify support tickets into 12 categories with 95% accuracy." That's the entire spec.
AI agents autonomously collect training data, design reward functions, choose hyperparameters, run experiments, evaluate results, and iterate until the target is hit.
Get a fine-tuned model endpoint ready for production. AutoSmith monitors performance and re-trains automatically when drift is detected.
Agents source, clean, and validate training datasets. They generate synthetic examples when real data is sparse, and filter for quality automatically.
For GRPO and RLHF workflows, agents write and iterate on reward functions. They test against edge cases and adjust until the signal is clean.
Full pipeline management: hyperparameter search, checkpoint selection, early stopping. Agents debug failing runs and retry with adjusted configurations.
Models are tested against holdout sets, adversarial inputs, and domain-specific benchmarks. Agents flag regressions before deployment.
Start small. Scale as your models grow.
Every company will have custom models. Most won't have ML teams. AutoSmith bridges that gap with agents that do the work humans can't scale.