Description
Brussle is a platform designed to enhance AI decision-making by calibrating decisions based on the outcomes you record. It ensures that each decision model's confidence is adjusted according to your specific data, applying corrections only when they outperform the model on held-out outcomes. This approach helps in reducing overconfidence in AI models, as demonstrated with models like gpt-6-luna and Jev, where calibration errors were significantly reduced.
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The platform supports TypeScript and Python SDKs, allowing developers to integrate Brussle's capabilities into their existing systems. It offers a flexible pricing model where users pay per decision, with prepaid credit options starting at $19 per month. The platform is designed to re-run decisions when records change and refit them when the model changes, ensuring that decisions remain accurate and relevant over time.
Brussle is particularly beneficial for organizations that require precise decision-making capabilities, such as those in data-driven industries. It provides tools for developers to build and ship decisions efficiently, with features like tenant namespaces, webhook endpoints, and outcome tools. The platform also supports per-customer training, ensuring that decisions are tailored to specific organizational needs.
With a focus on privacy and data security, Brussle ensures that personal data is handled responsibly, with encryption and access controls in place. It also offers international data transfer options compliant with European standards. Overall, Brussle provides a robust solution for organizations looking to enhance their AI decision-making processes with outcome-based calibration.
- CategoryMLOps & Model Deployment
- Pricing ModelPay-per-decision
- DeploymentCloud
- PlatformsCloud
Brussle's Core Features
Outcome-based AI decision calibration
TypeScript and Python SDKs
Pay-per-decision pricing model
Re-runs on record changes
Refits on model changes
Tenant namespaces
Webhook endpoints
Per-customer training
How to use Brussle?
Define: Set up decision parameters
Integrate: Use TypeScript or Python SDK
Calibrate: Train on recorded outcomes
Deploy: Implement decisions in production
Monitor: Adjust based on model changes
Brussle's Use Cases
- Model Calibration
- Decision Re-running
- Per-Customer Training
- Outcome Evaluation
- Data Privacy Compliance
