Recalibration layer that fixes overconfident AI decisions using your own labeled outcomes, without retraining the model.
Evals page - every fit scored on held-out data
1 / 3Overview
What is Brussle?
Improve Jev's accuracy on your data
Brussle is a recalibration layer that improves the accuracy of AI decisions on your own data without changing model weights. You define a decision, the records it reads, and the correct-answer labels your team provides; Brussle trains a correction on those labels so overconfident model outputs are adjusted. Every fit is scored on held-out data, giving you audit evidence that the correction works, and when a record changes only the decisions that read it are automatically re-run. It supports Jev and gpt-6-luna, offers TypeScript and Python SDKs plus a web console, and keeps your data inside your own organization. Plans start at $19/month for the Developer tier with usage-based decision and storage fees.
Platforms and languages
Brussle Availability
Platforms
Languages
Capabilities
Brussle Key Features
Confidence recalibration
Recalibrates the confidence of every AI decision on your own data; model weights never change.
Outcome-trained fitting
You define a decision, the records it reads, and the correct-answer labels your team provides.
SDKs and web console
TypeScript and Python SDKs plus a web console for managing fits.
Held-out scoring
Every fit is scored on held-out data as audit evidence that the correction works; the model's original output is kept when a fit doesn't win.
Automatic re-runs
When a record changes, only the decisions that read that record are automatically re-run.
Best for
Who uses Brussle?
Developers and teams building AI decision features
Fix overconfident model decisions with labeled outcomes instead of retraining
Plans and access
Brussle Pricing
Paid
From $19/mo (Developer); usage-based
Common questions
Brussle FAQs
How do I get started?
Define the decision you want corrected, point Brussle at the records it reads, and provide correct-answer labels from your team. Brussle fits the correction on those labels.
Does Brussle retrain or change my model?
No. Model weights never change; Brussle learns a separate correction layer on top of the model's outputs.
How do I know the correction actually helps?
Every fit is scored on held-out data, and if a fit doesn't beat the model's original output the original is kept.
What happens when my data changes?
When a record changes, only the decisions that read that record are automatically re-run.
Where does my data live?
Your data stays inside your own organization; Brussle only processes the records you point it at.
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