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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 promotes it — all automatically. Your model improves in production without any action from you.

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How it works

The Adaptive Inference loop runs continuously in the background once enabled:
1

You serve inference via Pioneer

You call POST /inference (or the OpenAI-compatible endpoint) as normal. Pioneer serves your model and handles all traffic.
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

Model behavior is automatically evaluated

Pioneer runs continuous evaluation against the captured traces to measure current model performance. This establishes a baseline before any retraining begins.
4

Training data is generated and a new checkpoint is fine-tuned

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.
5

The improved checkpoint is promoted

The new checkpoint is evaluated against the baseline. If it performs better, Pioneer promotes it automatically. Your model_id continues to point to the same endpoint — the underlying model has simply improved.

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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: 
curl -X POST https://api.pioneer.ai/inferences/YOUR_INFERENCE_ID/feedback \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "verdict": "incorrect",  
    "corrected_output": {
      "entities": [
        {"text": "Tim Cook", "label": "person", "start": 10, "end": 18},
        {"text": "Apple", "label": "organization", "start": 0, "end": 5}
      ]
    }
  }'
Retrieve a list of your past inferences to find IDs for follow-up: 
curl "https://api.pioneer.ai/inferences?model_id=YOUR_JOB_ID&limit=50" \
  -H "X-API-Key: YOUR_API_KEY"
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.

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Enabling Adaptive Inference

Dashboard: Log in to pioneer.ai, navigate to your model, and toggle on Adaptive Inference from the model settings page. Enterprise: For custom retraining schedules, feedback pipelines, or dedicated infrastructure, contact the Pioneer team directly.
Adaptive Inference is available on the Research and Custom (Enterprise) plans. It is not included on the Free or Pro plans. Upgrade at pioneer.ai → Settings → Plan, or reach out for enterprise pricing.

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Next steps