Our advanced evaluation and observability tools help teams maintain quality, reliability, and speed throughout the AI application lifecycle. Overview

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1. Experiment

We have a Playground++ built for advanced prompt engineering, enabling rapid iteration, deployment, and experimentation.
  • Organize and version their prompts effectively
  • Deploy prompts with different deployment variables and experimentation strategies without code changes
  • Connect with databases, RAG pipelines, and prompt tools seamlessly
  • Simplify decision-making by comparing output quality, cost, and latency across various combinations of prompts, models, and parameters

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2. Evaluate

Our unified framework for machine and human evaluations allows you to quantify improvements or regressions and deploy with confidence.
  • Access a variety of off-the-shelf evaluators through the evaluator store
  • Create custom evaluators suited to specific application needs
  • Measure quality of prompts or endpoints quantitatively using AI, programmatic, or statistical evaluators
  • Visualize evaluation runs on large test suites across multiple versions of prompts or endpoints
  • Human evaluations can be conducted for last-mile quality checks and nuanced assessments

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3. Observe

The observability suite empowers you to monitor real-time production logs and run them through periodic quality checks to ensure production quality.
  • Create multiple repositories for multiple apps for your production data that can be logged and analyzed using distributed tracing
  • Live issues can be tracked, debugged, and resolved quickly
  • In-production quality can be measured using automated evaluations based on custom rules
  • Datasets can be curated with ease for evaluation and fine-tuning needs

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4. Data engine

Seamless data management for AI applications allows users to curate and enrich multi-modal datasets easily for evaluation and fine-tuning needs.
  • Import datasets, including images, with a few clicks
  • Continuously curate and evolve datasets from production data
  • Enrich data using in-house or Maxim-managed data labeling and feedback
  • Create data splits for targeted evaluations and experiments