How to reduce LLM costs in AI customer support without cutting quality
A practical cost model for AI support agents: prompt caching, model routing, and context trimming, with the arithmetic per 10,000 tickets.
45 stories
A practical cost model for AI support agents: prompt caching, model routing, and context trimming, with the arithmetic per 10,000 tickets.
Fine-tuning vs RAG for support agents: the cost, staleness, and update-frequency arithmetic that makes retrieval the default choice for most teams.
Reduce a support ticket backlog with a triage-first playbook: sort the queue before adding headcount, with a 30 day sequence.
Manual QA scores 1 to 3 percent of tickets. AI can score all of them. Here is a rubric, a rollout plan, and the failure modes to watch.
Customer support CSAT benchmarks by channel and industry, why survey timing skews scores more than AI does, and how to read your own number.
Resolution rate vs deflection rate explained with formulas, denominators, and a worked example showing the same month reads 72% or 38%.
AI customer support ROI explained with real adoption data, a payback formula, and a worked example a CFO can actually sign off on.
AI support tone of voice guide: why robotic tone is a prompt problem, and the QA rubric that fixes it without changing models.
What a SOC 2 report actually attests to, what it leaves out, and the four questions to ask a support AI vendor before you sign.
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