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

A/B Testing

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.

Feature Flags

Disable models or entire features with the flip of a switch. Prevent faulty models from impacting production.

How it works

Features 01

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.

Features 02

Monitor ongoing experiments

We'll help you achieve statistical significance while giving you real time information on performance.

Features 03

Understand what retrieval methods improve performance.

Find underperforming data and convert it to training data. Debug LLM output through retrieved context

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