Ending first
Stop paying generative models to decide what something is. Use a fast decision engine for routing. Call an expensive System-2 model only when the work needs full-text synthesis or hard reasoning. Most operational volume is classification. Price it that way.
One repeatable line
Classify cheap. Generate rare.
Opening scene
A service firm runs every inbound email through a large language model. Invoice or not. Ticket tier. Owner. Each call burns tokens and waits three to five seconds. The queue backs up. The monthly bill climbs. Ninety percent of the answers were labels a specialist classifier could have returned in under half a second.
Situation
Generative reasoning is the wrong tool for high-volume, near-deterministic decisions. You pay for tokens you do not need. You add latency that clients feel as slow operations. Budget and patience both run out.
Action
Build a hybrid front door. Jev classifies. Make routes. Generative models and humans handle the thin slice that needs them.
Chapter 1 – Put Jev at the front door
Inside each Make scenario, call Jev first. Pass state. Ask typed questions (choice, score, noul). Get categories and confidence in one pass, typically under 500 milliseconds, at a fraction of a cent per call relative to full generative turns.
Must-remember: the front door decides direction, not prose.
Chapter 2 – Execute the routine path without an LLM
Chapter 3 – Reserve generative spend for the hard 10 percent
When Jev scores low confidence, or the task needs synthesis (a drafted reply, a summary, a negotiation brief), Make routes to a generative LLM or to a person. That is where token spend earns its keep.
Must-remember: generative cost belongs on ambiguity and writing, not on routing.
Recap
Jev classifies fast and cheap. Make branches. LLMs and humans take the minority of cases that need them. Pipelines speed up. Token overhead collapses toward the work that actually requires generation.
Ask
Audit one high-volume Make scenario. Count how many steps only need a label. Move those steps to Jev. Cap generative calls to synthesis and low-confidence exceptions.
Outcome
Operational AI cost tracks difficulty, not volume. Latency falls on the path most work takes. Finance can see a routing line and a generation line instead of one opaque LLM invoice.
For standard labels above your confidence bar, Make updates systems, assigns owners, and continues the workflow. No generative call. No multi-second wait.
Must-remember: routine volume should never wake a System-2 model.
Mid-market operators need routing: invoices, support tiers, CRM owners, which playbook to run. Make scenarios can orchestrate that volume. Generative LLMs are easy to drop into every step.