Show HN: Empromptu.ai – Solving the production reliability crisis in AI

After burning through thousands of credits on AI builders, we kept hitting the same wall: applications that worked in demos but crashed in production. The core issue isn't the building process - it's that most AI applications plateau at 60-70% accuracy, which makes them unusable for real users.

We realized these aren't actually "AI app builders" - they're website builders with ChatGPT wrappers. The fundamental architecture problems:

- Context Amnesia: Most builders suffer from conversation state loss, forcing users to repeat information and burning credits on iteration cycles. - Static Prompt Bloat: App Builders try to handle edge cases by cramming everything into massive 5-page prompts, which actually confuses LLMs and degrades performance. - Black Box Optimization: No granular control over individual components or transparent performance metrics.

Our technical approach centers on dynamic AI response optimization architecture:

1. Context Engineering: Persistent conversation memory with intelligent context discovery eliminates the repeat-and-iterate problem

2. Real-time Prompt Selection: Instead of one massive prompt, we maintain specialized prompt families and dynamically select optimal ones based on input characteristics (travel chatbot automatically switches between LAX context for LA vs Pearson for Toronto)

3. Individual Task Optimization: Granular control over each workflow component with transparent scoring metrics (you can optimize payroll queries separately from HR policies)

This consistently achieves 98% accuracy vs industry 60-70% - and we can demonstrate this live with side-by-side comparisons.

But solving accuracy alone wasn't enough. We also needed complete production infrastructure:

Full AI Stack: RAG, LLM operations, real backends with dynamic optimization (not just hosted demos)

Production Deployment: Docker containers, GitHub integration, on-premise options

Performance Transparency: Visible quality scores, edge case identification, systematic optimization

The result: Technical teams can build production-ready AI applications without dedicated ML expertise, while maintaining the control and visibility needed for business-critical deployments.

Technical founders and developers: Try it at https://builder.empromptu.ai

We'd love feedback from the HN community, especially if you've hit similar production reliability problems or have thoughts on the architectural approach.