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Most fine-tuned models are static: you train once, deploy, and watch accuracy drift as real-world inputs diverge from your training data. Adaptive Inference breaks that pattern. Pioneer monitors your live inference traffic, identifies high-signal examples, generates training data, fine-tunes a new checkpoint, evaluates it, and helps you promotes it so your model improves in production.

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How it works

Pioneer’s Deep Research agent curates a training dataset, a fine-tuning job runs, and the best checkpoint is evaluated before
anything touches production. You control when a new model version gets promoted. :
1

You serve inference via Pioneer

You call POST /inference (or the OpenAI-compatible endpoint) as normal. Inferences are logged automatically and accessible via GET/ inferences.
2

Pioneer captures high-signal traces

As traffic flows through, Pioneer monitors inference results and identifies examples that are ambiguous, low-confidence, or otherwise informative for improving the model. These traces are stored in your inference history and are accessible via GET /inferences.
3

A new checkpoint is trained from your corrections

Pioneer uses the high-signal traces — plus any explicit feedback you provide — to generate additional labeled training data. It then fine-tunes a new checkpoint of your model using that data.
4

Performance is benchmarked before promotion

After training completes, Pioneer automatically runs an evaluation against a held-out dataset and reports F1, precision, and recall. Pioneer runs continuous evaluation against the captured traces to measure current model performance. This establishes a baseline before any retraining begins.
5

The improved checkpoint is promoted

The new checkpoint is evaluated against the baseline.Review the evaluation results and deploy the best checkpoint from the deployment page Your model_id continues to point to the same endpoint — the underlying model has simply improved. The deployment page highlights the best-performing checkpoint to make this decision easy.

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Submitting feedback

Your explicit corrections are the highest-quality signal for Adaptive Inference. After receiving an inference result, submit feedback using the inference ID:
Retrieve a list of your past inferences to find IDs for follow-up:
Feedback you submit is incorporated into the next training cycle. The more corrections you provide, the faster the model converges on the behavior you want. Enterprise: For custom retraining schedules, feedback pipelines, or dedicated infrastructure, contact the Pioneer team directly.
Unlimited Adaptive Inference is available on Pro, Research, and Custom (Enterprise) plans. It is not included in the Free plan. Upgrade at pioneer.ai → Settings → Plan, or reach out for enterprise pricing.

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Next steps