You know what inputs you have and what outputs you need. Define your function and we'll do the needful: structured outputs, evaluation, and automatic optimization in one place.
Your first function takes ~2 minutes
Most teams spend 10+ hours per week on prompt iteration, juggling LangSmith, Instructor, Braintrust, and custom monitoring.
Your LLM sometimes returns the wrong format, missing fields, or unexpected results. You spend hours debugging.
Every prompt iteration costs real money. You're using GPT-4 when Haiku would work just fine. Cache hits: 0%.
You've got a working prototype, but how do you deploy it? Monitor it? Know when it breaks? Improve it over time?
No prompt engineering PhD required. No 47 knobs to turn. Define your inputs and outputs, and we'll do the needful.
Use JSON Schema to define your inputs and outputs. We validate every response and catch format errors before they hit production.
{
"type": "object",
"properties": {
"risk_score": { "type": "number" },
"is_fraud": { "type": "boolean" }
}
}Add evaluation examples, click "Do the needful." We test 50+ prompt and model combinations to find the cheapest option that hits your accuracy target. Automatic optimization, not 47 knobs to turn.
Before
$0.003/call
After
$0.0018/call
Add examples of good and bad outputs. Rate them as Good/Fair/Poor. We track accuracy over time and help you improve systematically.
Most users add 10-20 examples over 2 weeks. That's enough to unlock automatic optimization.
See errors, costs, and cache savings at a glance. Set alerts for error rates. Export failed examples to improve your function.
Cache hit rates typically reach 60%+ within a week, saving hundreds of dollars per month.
From idea to production in 30 minutes
Name it, paste your input/output schemas. We infer schemas from examples if you prefer. Choose a template or start from scratch.
Import from CSV, copy from production logs, or generate synthetic examples. Rate outputs as Good/Fair/Poor. 5 examples unlocks optimization.
Click "Optimize." We test 50+ variants using DSPy. Typical results: 85%+ accuracy, 40-70% cost reduction. Deploy the best variant in one click.
Get your API endpoint. Copy the integration code (Python, Node.js, Go, Ruby). Monitor errors, costs, and cache savings from the dashboard.
Median cost reduction (no, really)
Time to first working function
Typical accuracy after optimization
Define your inputs and outputs. We'll handle the complexity. Free tier includes 1,000 calls per month.
Get Started FreeNo credit card required