LLM cost optimization in Nashville.

The technical reality of operations spend in Nashville.

Questions from Nashville buyers.

At claims and prior-auth volume, what should each item cost through the pipeline?

Engineered targets: one to three cents per prior-auth packet assembled, under a cent per claim correspondence piece classified and drafted, with unoptimized pipelines running five to fifteen times those numbers. The composition at Nashville's revenue-cycle volumes: payer policies and approved language are the heavy context (cacheable at the 90-percent discount as PHI-free prefixes), the case record is the modest per-item payload, and the assembly-and-draft work is template-grounded (exactly where mid-size models match frontier output, provable on specialist-graded samples). The recurring waste pattern: policies re-billed uncached per case, frontier defaults from the pilot era never revisited, and overnight queues at synchronous prices. At an operation touching hundreds of thousands of items annually, the per-item delta funds positions, which is why the audit converts everything to the units your VP of revenue cycle already manages by.

Payer policies change constantly. Does that break the caching strategy?

No; it makes versioning the strategy. Payer-policy content is heavy and stable between updates, which is the cache's favorite shape: each payer's current policy set lives as a canonical, versioned, PHI-free prefix (UnitedHealth-shaped payer A, version 2026-05, cached as one generation), billing at the discounted rate across every case touching that payer until the version increments. Updates are scheduled cutovers rather than drift: the new policy version deploys as a new cache generation, the old one expires naturally, and the version identifier rides in each case's audit line, which your compliance team wants regardless. The discipline that protects the saving is the one your policy librarians already believe in: one canonical copy per payer, versioned releases, no informal edits fragmenting the cache into near-duplicates. Operations with twenty-payer books typically see input costs drop by more than half from this structure alone, with the policy-update workflow unchanged except for being slightly more honest.

Do auto-send thresholds on routine messages actually reduce model spend, or just labor?

Both, through a mechanism teams rarely predict. The labor saving is obvious: messages confident enough to auto-send skip the review queue. The model saving is structural: pipelines without earned auto-send tiers compensate with belt-and-suspenders generation (longer prompts, bigger models, second-pass review drafts) applied to every message because any message might matter; once accuracy is measured per message class and auto-send is earned by the routine tiers (confirmations, standard instructions, status updates), the pipeline relaxes to right-sized models with lean prompts on exactly the traffic that dominates volume. The hospitality side of Nashville sees the same physics: guest-message confirmations auto-send on small models while anything emotional routes to humans with a draft. The design work is the threshold governance: per-class accuracy measured continuously, auto-send revoked automatically on regression, and the audit trail recording which tier handled each message. Spend drops, response times drop, and the humans concentrate where judgment lives.

The board wants ROI in staff-hours, not tokens. How does the report translate?

Directly, because staff-hours are the currency revenue-cycle and hospitality operations actually run on. The translation layer: each workflow's AI cost per item sits beside the minutes of staff time it displaces (packet assembly that took 25 minutes now takes 4 of review; correspondence triage that consumed a morning now consumes the exceptions), priced at fully loaded hourly rates your finance team supplies. The report's front page reads in those units: monthly AI spend of $X displaces Y staff-hours worth $Z, net monthly value, payback on the optimization fee in weeks. The token economics live in the appendix for the engineers. Two honesty rules keep the number credible: displaced hours count only where the queue data shows actual throughput change (not theoretical capacity), and AI costs are fully loaded too (inference plus the review minutes that remain). Boards fund the second project when the first one's math survived their own controller's audit, and we build the report for that audit.