After spending months building AI applications, one thing became painfully obvious:
The hardest part isn't getting an LLM to work.
It's getting it to work reliably.
We kept running into the same issues:
- Hallucinations
- Prompt injections
- Silent failures
- Unpredictable agent behavior
- Expensive debugging cycles
So we started building a reliability layer for AI applications.
Over the last few months we've built Crukx, which combines:
• Hallucination detection and correction
• Self-healing workflows
• Autonomous codebase auditing
• Prompt optimization
• Runtime guardrails
One result we're particularly happy with:
Our hallucination benchmark went from roughly 67% hallucination rate to around 11% using a layered verification and correction pipeline.
We're still early and there are plenty of things that don't work perfectly yet.
I'd genuinely love feedback from people building with LLMs:
What's been your biggest reliability challenge in production?
Product Hunt launch: https://www.producthunt.com/products/crukx?utm_source=other&utm_medium=social
Happy to answer technical questions about the architecture and benchmarks.
Top comments (1)
👋 Hi Devs!
I'm Shafwan, one of the founders of Crukx.
Over the last year, we've been building AI products and running into the same frustrating problem: AI works great in demos, but production is a different story. Hallucinations, unpredictable behavior, broken agent workflows, and hidden failures make it difficult to trust AI systems at scale.
We built Crukx to solve that.
Crukx is an AI reliability platform that automatically tests, verifies, and monitors AI applications before failures reach your users. It uses an autonomous swarm of specialized agents to stress-test prompts, audit workflows, detect hallucinations, generate E2E tests, and continuously monitor reliability in production.
A few things we're excited about:
✅ Reduced hallucination rates from 50% to 5% on our adversarial benchmarks
✅ Autonomous AI testing with our 7-agent swarm
✅ Built-in hallucination detection and verification layers
✅ MCP integrations for Cursor, Claude, VS Code, Windsurf, and more
✅ Real-time monitoring of quality, reliability, and cost
We're still early, and we'd love your feedback.
What is the biggest challenge you've faced when deploying AI applications in production?
We'll be around all day answering questions and discussing the future of reliable AI. Thanks for checking out Crukx 🚀