Online experimentation for LLMs, retrieval, and recommendations
We help you manage data, collect user feedback, and evaluate new models using offline metrics and online A/B testing. DeployQL enables a direct understanding of how AI impacts your bottom line.
ML in production is more than serving the model
DeployQL simplifies production requirements to enable your ML team to focus on business impact.
Streamlined Offline Evaluation
DeployQL simplifies the process of evaluating AI features, saving you time and effort. Use our LLM judges against your datasets.
User Focused Feedback
Use our platform to collect user feedback, ensuring that your models exceed expectations.
Direct Impact on Profitability
Define fallback behavior if a model fails to run. Automatic rollouts using guardrail metrics to prevent experiments from impacting production.
Centralized tooling to increase ML iteration speed
Rapidly experiment, iterate, and deploy while measuring business impact
DeployQL will run your A/B testing for you. Capture metrics and guardrails, and DeployQL will roll out your model.
Monitoring and Logging
Explore your data to know exactly how to improve your model.
Disable models or entire features with the flip of a switch. Prevent faulty models from impacting production.
How it works
Define your experiment parameters
Experiment built on top of feature flags for full understanding of production behavior. We'll help you define the right experiment settings.
Monitor ongoing experiments
We'll help you achieve statistical significance while giving you real time information on performance.
Understand what retrieval methods improve performance.
Find underperforming data and convert it to training data. Debug LLM output through retrieved context