How enterprises use Pnyx to route AI workloads to the right model — across providers, industries, and outcomes.
Intra-provider routing across the GPT-5 family for a retail bank
A mid-size bank runs five AI workloads through OpenAI — all on manually assigned models. Pnyx evaluates each prompt, identifies over-provisioned and under-provisioned tasks, and routes to the right GPT-5 tier. Three workloads route down. Two route up.
OpenRouter + Pnyx for a CRM platform with seven AI-embedded features
A mid-market CRM platform routes AI through OpenRouter across four providers — each feature team picked its own model independently. Pnyx adds the decision layer: three features route down, two route up, two get dynamic per-request routing.
Submit production prompts, get a data-driven model selection report
A P&C carrier submits its production prompts and Pnyx analyzes every one — which models fit, what percentage goes to each tier, and where current assignments are costing more than they should. The data shows the spend profile is inverted. Evidence replaces assumptions.
Your workloads change. Pnyx keeps watching.
A health system's AI workloads drift as patient volumes shift, protocols change, and new models launch. Pnyx continuously evaluates production prompts against the full model landscape — including models outside the current stack — and fires alerts when a better fit emerges. Every decision stays with the governance team.
Connect vendor or custom agents — get capability profiles across your actual work
Law firms are choosing agents, not just models — Harvey, CoCounsel, Spellbook, custom builds. Pnyx evaluates each one against the firm's actual task patterns and returns a capability profile in 24–72 hours: task completion, cost per task, error patterns, and comparative ranking. Data replaces demos.
More use cases across additional providers, industries, and outcomes — coming soon.
