This is the exact problem CognOS was built to solve.

  99% reliable means you still can't remove the human from the loop — because you never know which 1% you're in. The only way to actually trust output is to attach a verifiable confidence   
  signal to each response, not just hope the aggregate accuracy holds.                                                                                                                        
                                                                                                                                                                                            
  We built a local gateway that wraps every LLM output with a trust envelope: decision trace, risk score, and an explicit PASS/REFINE/ESCALATE/BLOCK classification. The point isn't to make 
  LLMs more accurate — it's to make their uncertainty legible so the human knows when to step in.

  Open source if you want to look at the architecture: github.com/base76-research-lab/operational-cognos