Building production-ready AI systems (RAG, copilots) — what actually breaks after MVP

Hey everyone 👋

We’ve been building production-ready AI systems at Skyllect — mostly RAG pipelines, AI copilots, and SaaS products.

One pattern we keep seeing:
getting a demo to work is easy… getting it to work reliably in production is not.

The biggest issues we’ve run into:

• hallucinations when context isn’t well-grounded
• API costs scaling faster than expected
• systems breaking under real user behavior (edge cases)

What’s worked better for us is focusing less on prompts, and more on:

→ retrieval design (chunking, reranking)
→ system architecture (async, caching)
→ evaluation early, not after launch

Curious—what’s been the hardest part for you when moving from AI demo → real product?