"Small models for big workloads: when cheap wins"
Most of the tokens flowing through a production AI system are doing boring work: classify this, route that, pull out three fields. Pointing a frontier model at all of it is the most common cost mistake I see — and the fix is measurable, not speculative.
Most of the tokens flowing through a production AI system are doing boring work. Classify this ticket. Route that email. Pull the invoice number, date and total out of this document. Decide whether this message needs a human. Teams point their most expensive model at all of it, usually because that is what the prototype used and nobody revisited the decision once volume arrived. The bill that follows is not a model problem; it is a routing problem.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
Book a free consultation