Coolhand

Actionable Cost & Quality Intelligence for LLM Workflows

Cut our costs for one AI call by over 90%!

The quality evaluation loop transformed how we deploy models & measure improvements. No more guessing.

I honestly didn't realized all the LLM workloads we were running. Now we know what's working and what's not.

Finally, we have AI evals focused on user satisfaction -- not just abstract metrics.

Instant Intelligence

  • πŸš€ Drop into Node.js, Ruby, or Python apps instantly
  • πŸ“Š Unified dashboard for all LLM workflows
  • πŸ“ˆ Track models, performance, costs, and quality
  • 🎯 Organization-wide visibility and trends

Close the Quality Loop

  • πŸ’¬ Transform user feedback into automated evaluations
  • πŸ§ͺ Test LLM versions against real-world scenarios
  • πŸ›‘οΈ Prevent regressions before deployment
  • πŸ”„ Continuous quality improvement loop

No More Surprise AI Bills

  • 🚨 Alerts for cost-saving model opportunities
  • πŸŽ›οΈ Control token bloat and usage patterns
  • πŸ“Š Forecast AI expenses with precision
  • πŸ’‘ Intelligent cost optimization recommendations

Enterprise-ready

  • πŸ₯ HIPAA compliant for healthcare applications
  • πŸ‘¨β€βš•οΈ Healthcare specialists for clinical evaluations
  • πŸ”’ Enterprise-grade security and compliance
  • βš–οΈ Ready for any regulated industry

Quick Installation

Get started in seconds with your preferred language

1. Install

# Install the Coolhand Node.js client
npm install coolhand-node

2. Enable Auto-Monitoring

// Add this ONE line at the top of your main file
require('coolhand-node/auto-monitor');

3. Create Feedback

// Track LLM response quality
const coolhand = new Coolhand({ apiKey: 'your-key' });
await coolhand.createFeedback({
original_output: 'LLM response',
revised_output: 'Same response but with user corrections',
like: true
});

Zero refactoring required! Works with OpenAI, Anthropic, and other AI providers automatically.

Ready to take control of your AI workflows?

Join organizations already using Coolhand to optimize their LLM implementations.