TierZero is the layer that quietly keeps MindPath BI’s drafting surface working at scale. We built it because we kept hitting the same three failure modes everyone running LLM workloads at scale eventually runs into — the section below names them.
What we built to solve them is the part we’re keeping close. If you’ve lived the same problems on your own stack, drop us a note.
Three failure modes show up at every LLM-infrastructure team we’ve traded notes with. The shape of the problem is public industry knowledge in 2026; you’ve probably hit all three on your own stack.
We’re comfortable naming the problems. What we built to address them is the part we’re keeping close for now.
The simple decisions inside every assistant turn — triaging an intent, picking which channel a thread belongs to, grading whether a draft is good enough — get dispatched to expensive reasoning models. Their hidden chain-of-thought burns the budget; the visible answer is often worse than a one-cent classifier’s would have been. The bill arrives anyway.
Vector retrieval runs across millions of corpus chunks for every turn, even when the actual answer lives in one record in your ERP, one invoice in your billing system, one ticket in your support tool. Tokens spent retrieving are wasted; tokens spent assembling are wasted; tokens spent answering are wasted. Latency suffers in the same direction.
Your dashboard reports one cost. The provider invoice arrives three days later with a different number. Neither can be reconciled to a specific feature, a specific user, or a specific turn. Operators end up flying blind, and the CFO finds out about a 4× spike on Tuesday morning, not Tuesday afternoon.